A multi-store unified inventory and real-time order collaborative management method

Abstract

The present application relates to a kind of multi-store unified inventory and real-time order collaborative management method, by constructing only the snapshot chain of recording state difference and version hash, the efficient tracking and tamper-evident of inventory state change is realized.System can be based on order flow and store operation signal triggers inventory rebalancing event, and dynamically constructs store collaborative graph in combination with multidimensional data such as historical allocation, logistics, load, intelligently determines and optimizes inventory allocation path.Prefix consistency check is used to ensure the timeliness and accuracy of allocation, and local emergency performance and asynchronous compensation are supported for scenarios that cannot meet the conditions.Sliding window and local persistence mechanism are used to realize the dynamic management and recovery of snapshot chain.The scheme improves the accuracy, efficiency and security of inventory allocation, realizes millisecond-level response and full traceability, and has good scalability and disaster recovery capability.

Description

Technical Field

[0001] This invention relates to the field of e-commerce supply chain inventory collaborative management technology, and in particular to a method for collaborative management of unified inventory and real-time orders across multiple stores. Background Technology

[0002] With the rapid development of e-commerce platforms and the widespread adoption of diversified store operation models, achieving efficient and collaborative management of unified inventory across multiple stores has become a crucial direction for the digital upgrade of the supply chain. Currently, multi-store inventory management solutions in the industry generally adopt a centralized database architecture, using distributed transactions and row-level or table-level locking mechanisms to ensure the consistency and accuracy of inventory allocation. Some advanced platforms have introduced inventory allocation methods based on priority queues, polling scheduling, or pre-allocation strategies to improve concurrent allocation capabilities, supplemented by historical data-driven dynamic weighting methods to optimize inventory flow. Many enterprises also combine event-driven asynchronous message queues to synchronize inventory changes to various business modules, enhancing system decoupling and scalability.

[0003] However, existing multi-store unified inventory management solutions still face several bottlenecks in scenarios with high-concurrency orders and complex inventory allocation decisions. On one hand, traditional centralized databases or distributed locking mechanisms are prone to lock contention and write amplification effects when resource contention is severe, leading to a significant increase in inventory reallocation response latency. This makes it difficult to meet the performance requirements of second-level or even millisecond-level inventory allocation during peak business periods such as e-commerce promotions and flash sales. Simultaneously, scheduling methods based on priority queues or pre-allocation ratios have limited real-time dynamic response capabilities to sudden order fluctuations and store distribution value, often failing to balance maximizing order value with inventory turnover efficiency. Some platforms have attempted to introduce machine learning prediction models or strategy optimization methods, but in practical applications, model training and deployment cycles are long, and achieving millisecond-level closed-loop processing is difficult, making them unsuitable for high real-time requirements. Summary of the Invention

[0004] This application provides a method for unified inventory and real-time order collaborative management across multiple stores, aiming to solve the problem mentioned in the background art of being unable to simultaneously maximize order value and improve inventory turnover efficiency.

[0005] This application provides a method for collaborative management of unified inventory and real-time orders across multiple stores, specifically including:

[0006] S1: For product SKUs in a multi-store unified inventory management scenario, construct a memory snapshot chain based on the real-time available inventory share, dynamic value weight coefficient, and the type of the most recent allocation trigger event of each store;

[0007] S2: Generate an inventory rebalancing event based on the value assessment update signals in the new order data stream and store operation data stream generated by the order acquisition module;

[0008] S3: Construct a dynamic collaboration graph between stores, and perform local feasibility determination and candidate allocation path enumeration on the inventory rebalancing event based on the dynamic collaboration graph between stores, so as to generate a local feasibility determination report.

[0009] S4: Based on the local feasibility determination report, perform a prefix consistency check operation. If the check passes, generate an atomic new snapshot node. If the check fails, generate a temporary fulfillment instruction for the local store buffer pool.

[0010] S5: Append the atomized new snapshot node to the end of the memory snapshot chain to complete the state transition, and broadcast the snapshot submission notification to the relevant store gateway based on the updated memory snapshot chain, thereby forming a closed loop of cross-store inventory redistribution;

[0011] S6: For temporary fulfillment instructions in the local store buffer pool, enable the local store buffer pool of the corresponding store to perform temporary inventory deduction operations.

[0012] This application provides a method for collaborative management of unified inventory and real-time orders across multiple stores, which has the following beneficial effects:

[0013] This invention abandons traditional centralized locking mechanisms and distributed transaction coordination, constructing an event-driven, memory-based collaborative system. It employs declarative state management based on an immutable snapshot chain, maintaining a time-series snapshot chain for each SKU, significantly reducing the computational complexity of inventory reading and state merging. Inventory rebalancing events are processed asynchronously via a distributed event bus, decoupling business and decision flows. Single allocation decisions are completed in milliseconds, and the system maintains linear scalability even under tens of thousands of concurrent requests, significantly improving response speed and throughput under high-frequency order surges. Simultaneously, a dynamic collaborative graph integrating historical allocation success rates, logistics timeliness, and service load is constructed, replacing static weight or priority rules to achieve value-oriented, efficient matching between stores and enhance adaptability. The snapshot chain is constrained by a sliding window, ensuring memory control, and is persisted via memory-mapped files, balancing high performance and reliability. For allocation requests that cannot be immediately fulfilled, a local buffer pool is activated for temporary fulfillment and asynchronous compensation to avoid service interruption. The entire mechanism requires no external prediction models or reinforcement learning, does not rely on transaction locks, and achieves lightweight and highly responsive inventory collaborative management. It significantly improves robustness and maintainability in complex business scenarios, is suitable for high-concurrency and time-sensitive environments such as e-commerce promotions and chain retail, and has good engineering implementation value. Attached Figure Description

[0014] Figure 1 This is a main flowchart of a multi-store unified inventory and real-time order collaborative management method according to an embodiment of this application.

[0015] Figure 2This is a sub-flowchart of a multi-store unified inventory and real-time order collaborative management method according to an embodiment of this application.

[0016] Figure 3 This is another sub-flowchart of a multi-store unified inventory and real-time order collaborative management method according to an embodiment of this application. Detailed Implementation

[0017] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0018] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0019] Reference Figure 1 As shown, one embodiment of this application provides a method for collaborative management of unified inventory and real-time orders across multiple stores, specifically including:

[0020] S1: For product SKUs in a multi-store unified inventory management scenario, a memory snapshot chain is constructed based on the real-time available inventory share, dynamic value weight coefficient, and the type of the most recent allocation trigger event of each store.

[0021] S2: Generate inventory rebalancing events based on the value assessment update signals in the new order data stream and store operation data stream generated by the order collection module.

[0022] S3: Construct a dynamic collaboration graph between stores, and based on the dynamic collaboration graph between stores, perform local feasibility determination and candidate allocation path enumeration for the inventory rebalancing event to generate a local feasibility determination report.

[0023] S4: Based on the local feasibility determination report, perform a prefix consistency check operation. If the check passes, generate an atomic new snapshot node. If the check fails, generate a temporary fulfillment instruction for the local store buffer pool.

[0024] S5: Append the atomized new snapshot node to the end of the memory snapshot chain to complete the state transition, and broadcast the snapshot submission notification to the relevant store gateway based on the updated memory snapshot chain, thereby forming a closed loop of cross-store inventory redistribution.

[0025] S6: For temporary fulfillment instructions in the local store buffer pool, enable the local store buffer pool of the corresponding store to perform temporary inventory deduction operations.

[0026] This embodiment's multi-store unified inventory and real-time order collaborative management method abandons traditional centralized locking mechanisms and distributed transaction coordination, constructing an event-driven, memory-based collaborative system. It employs declarative state management based on an immutable snapshot chain, maintaining a time-series snapshot chain for each SKU, significantly reducing the computational complexity of inventory reading and state merging. Inventory rebalancing events are processed asynchronously via a distributed event bus, decoupling business flow from decision flow. Single allocation decisions are completed in milliseconds, and the system maintains linear scalability even under tens of thousands of concurrent requests, significantly improving response speed and throughput under high-frequency order surges. Simultaneously, a dynamic collaborative graph integrating historical allocation success rates, logistics timeliness, and service load is constructed, replacing static weight or priority rules to achieve value-oriented, efficient inter-store matching and enhance adaptability. The snapshot chain is constrained by a sliding window, ensuring memory control, and is persisted via memory-mapped files, balancing high performance and reliability. For allocation requests that cannot be immediately fulfilled, a local buffer pool is activated for temporary fulfillment and asynchronous compensation to avoid service interruption. The entire mechanism requires no external prediction models or reinforcement learning, does not rely on transaction locks, and achieves lightweight and highly responsive inventory collaborative management. It significantly improves robustness and maintainability in complex business scenarios, is suitable for high-concurrency and time-sensitive environments such as e-commerce promotions and chain retail, and has good engineering implementation value.

[0027] Furthermore, referring to Figure 1 In one embodiment, the multi-store unified inventory and real-time order collaborative management method further includes the following steps: S7: dynamically manage the length of the memory snapshot chain, and persist old snapshot nodes that exceed the preset change number threshold to the local SSD storage medium through memory-mapped files.

[0028] It can be explained that step S7, through dynamic management of the memory snapshot chain length, achieves an organic unity between controllable memory resources and traceable historical states. Specifically:

[0029] Precise control of memory usage: By using a sliding window strategy and a preset threshold for the number of changes (such as 300 times), the memory snapshot chain is limited to the active change window, avoiding memory overflow caused by unlimited growth, and ensuring the long-term stability and predictability of resources of the system.

[0030] Highly efficient persistent writing: Employing operating system kernel-level memory-mapped files (mmap) and zero-copy direct writing technology, combined with non-temporal storage instructions to bypass CPU cache pollution, and a 4KB aligned page write strategy adapted to SSD physical characteristics, significantly reducing memory copy overhead and write amplification effect in the I / O path, achieving millisecond-level low-latency persistence.

[0031] Extreme optimization of storage space: ZigZag variable-length integer encoding, relative time difference compression, and Protocol Buffers structured encapsulation are used in the serialization process, which reduces the size of archived data by more than 85% compared with the traditional JSON format, effectively reducing SSD storage usage.

[0032] Real-time reclamation of memory resources: After archiving is completed, the logic is unloaded and the physical memory is released for the persistent nodes, and the memory resources are returned to the system in real time to maintain the efficient retrieval and merging performance of nodes within the active window (O(log n) complexity).

[0033] High availability disaster recovery and rapid recovery: Register cross-process shared handles and power failure recovery index pointers, establish a two-way mapping between a streamlined memory snapshot chain and persistent archive files, support multi-process concurrent reading of historical states, and quickly rebuild the memory snapshot chain baseline from the SSD through recovery pointers after abnormal system restart, achieving fault recovery and zero data loss.

[0034] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the steps or stages in other steps.

[0035] Reference Figure 2 As shown, step S1 involves constructing a memory snapshot chain for product SKUs in a multi-store unified inventory management scenario, based on the real-time available inventory share, dynamic value weight coefficient, and the type of the most recent allocation trigger event for each store. Specifically, this includes:

[0036] S1.1: Obtain the real-time available inventory share data, dynamic value weight coefficient, and most recent allocation trigger event type for each store in the multi-store unified inventory management scenario for the target product SKU. Perform initial state aggregation processing on the real-time available inventory share data, dynamic value weight coefficient, and most recent allocation trigger event type to generate an initial inventory state baseline vector containing full business state information.

[0037] The product SKU is the smallest unit of inventory for a product; the dynamic value weight coefficient is a dynamic priority or importance value for a target SKU in each store. It is not a fixed value, but a weight index calculated based on real-time business factors (such as the store's sales rate, unit profit, logistics timeliness and cost, service level, historical fulfillment success rate, etc.); the most recently allocated trigger event type records the business event category that caused the last change in the current inventory status of the store (such as inventory deduction, increase, transfer, etc.), which is usually mapped to a standardized enumeration code (e.g., order fulfillment, cross-store transfer, return to warehouse, replenishment and shelving, order cancellation, etc.).

[0038] For target product SKUs in a multi-store unified inventory management scenario, real-time available inventory share data, dynamic value weight coefficients, and the identifier of the most recent allocation trigger event type are retrieved in parallel from a distributed cache cluster for each associated store. The acquired multi-source heterogeneous data is timestamped to remove lagging, dirty data caused by network jitter, ensuring all input parameters are within the same logical time slice. The cleaned inventory share values ​​of each store are arranged in store ID index order to construct the original inventory component vector; the corresponding dynamic value weight coefficients are extracted simultaneously to form the value assessment component vector; and the most recent allocation trigger event type is mapped to a standardized enumeration code to generate an event context vector. Tensor concatenation technology is used to merge the above three component vectors along the feature dimension to construct an initial multidimensional state matrix containing full business state information. Normalization is performed on the initial multidimensional state matrix to eliminate numerical differences between data of different dimensions, ensuring the stability of subsequent difference calculations. A linear transformation is used to flatten the normalized multidimensional state matrix into a one-dimensional array, generating an initial inventory state baseline vector containing full business state information. Through the above aggregation processing method, the scattered real-time status data of multiple stores is transformed into a structured initial inventory status baseline vector, realizing the unified representation and standardized output of multi-source status information.

[0039] For example, the system manages the inventory status of SKU-C in three stores: P1, P2, and P3. It reads the inventory data from the Redis cluster: P1 has 100 units, weight 1.2, and event type 0; P2 has 50 units, weight 1.5, and event type 1; and P3 has 80 units, weight 1.1, and event type 0. It constructs an inventory component vector [100, 50, 80], a value component vector [1.2, 1.5, 1.1], and an event component vector [0, 1, 0]. These are concatenated to form a 3x3 state matrix, which is then normalized and flattened into a one-dimensional baseline vector [0.55, 0.27, 0.44, 0.40, 0.50, 0.36, 0.0, 1.0, 0.0]. This vector completely records the current full business status and serves as the sole benchmark for subsequent differential coding, ensuring the accuracy and consistency of state tracking.

[0040] S1.2: Based on the initial inventory state baseline vector, the numerical change of the current inventory state relative to the previous historical inventory state is calculated using the differential coding method. The numerical change is then subjected to sparse compression processing to generate a lightweight difference vector that only records state change information.

[0041] The system receives the initial inventory status baseline vector generated by S1.1. This vector contains the real-time available inventory share, dynamic value weight coefficient, and event type identifier for the target SKU across multiple stores. It determines whether the current moment is the first snapshot node generated after system initialization. If yes, it performs full state serialization encoding to construct a baseline difference vector; otherwise, it loads the inventory status baseline vector from the previous historical moment as a reference baseline and performs incremental difference calculation. For non-first node scenarios, it extracts the numerical components of each dimension from the baseline vector to construct the current moment's state feature matrix and the reference feature matrix from the previous moment. It uses element-wise subtraction to calculate the numerical deviation between the current state feature matrix and the reference baseline matrix, obtaining the original difference data set. It performs zero-value filtering on the original difference data set, removing redundant dimensions that have not undergone state changes, retaining only valid indices and value pairs with non-zero changes. It uses run-length encoding to compress the valid index sequence, merging consecutive non-zero index intervals into a start position and length tuple to reduce storage overhead. Delta encoding is applied to non-zero numerical pairs to calculate the second-order difference between adjacent changes, further reducing the bit width of the numerical data. The compressed index tuples are concatenated with the encoded numerical stream to form a compact binary data block. Through sparse compression, the full state update is transformed into a lightweight difference vector that only records changes.

[0042] S1.3: Receive the lightweight difference vector, and use a cryptographic hash algorithm to perform a one-way hash operation on the lightweight difference vector and the previous node identification information to generate a version hash value to ensure the immutability and version uniqueness of the data.

[0043] The system receives a lightweight difference vector generated by S1.2, which contains a sparsely compressed inventory status change index tuple and a second-order difference numerical stream. It extracts the timestamp identifier of the current snapshot node to be generated and the version hash value of the preceding historical snapshot node, serving as the basic anchor information for constructing a causal chain structure. The binary data block of the lightweight difference vector is concatenated byte-level with the preceding node identifier information to form the original message payload for hash calculation, ensuring the integrity and order consistency of the input data. The SHA-256 cryptographic hash algorithm engine is invoked to perform a one-way hash operation on the original message payload, generating a fixed-length 256-bit digest sequence through multiple rounds of nonlinear permutation and modular addition operations. This digest sequence possesses collision resistance; any minor tampering with the difference vector or preceding identifier will cause an avalanche effect change in the hash value, thus establishing data immutability. The generated 256-bit digest sequence is converted into a hexadecimal string format and defined as the version hash value of the current snapshot node, serving as a unique digital fingerprint identifying the inventory status version. By using a cryptographic hash algorithm, the difference vector from the previous step is transformed into a version hash value with cryptographic security properties, thereby enabling uniqueness verification and tamper-proof protection of node versions in the memory snapshot chain, ensuring that the state evolution during cross-store inventory collaboration is traceable and trustworthy.

[0044] S1.4: Based on the lightweight difference vector and the version hash value, perform a memory snapshot node encapsulation operation to assemble the lightweight difference vector and the version hash value into an independent data unit with time-series attributes to generate a lightweight immutable snapshot node.

[0045] S1.5: Obtain multiple lightweight immutable snapshot nodes arranged in chronological order, and use a linked list pointer mapping mechanism to logically link adjacent lightweight immutable snapshot nodes to form an initial memory snapshot chain structure that supports O(n) read and O(logn) merge operations.

[0046] Receive the lightweight, immutable snapshot node sequence generated by S1.4, which contains time-ordered difference vectors and version hashes. Construct a singly linked list index structure based on memory address mapping, assigning a unique physical memory address identifier to each snapshot node. Extract the memory start address of the snapshot node generated at the current moment and assign it to the Next pointer field of the previous tail node, establishing a unidirectional logical link from the previous node to the current node. Update the head and tail index registers of the global memory snapshot chain, pointing the tail pointer to the address of the newly appended snapshot node to ensure the continuity of the linked list structure. For O(n) read requirements, traverse along the Next pointer starting from the head node, accumulating the applied difference vector to the base state, and reconstructing the full inventory view at any time. For O(log n) merging requirements, maintain a balanced binary index tree synchronously during the linked list construction process, with the leaf nodes of the tree pointing to the key milestone nodes in the snapshot linked list. Calculate the accumulated state difference between adjacent milestone nodes and store it in the middle node of the index tree. When multiple historical states need to be merged, quickly locate the nearest common ancestor node through the index tree, and only perform accumulation operations on the difference vectors along the path. Bitwise operations are used to optimize pointer jump logic, reducing cache misses and improving memory access locality. Through the collaborative work of a linked list pointer mapping mechanism and a balanced index tree, discrete snapshot nodes are organized into an initial memory snapshot chain structure that supports efficient random access and range merging, achieving millisecond-level state backtracking and merging performance.

[0047] Reference Figure 3 As shown, step S2 involves generating an inventory rebalancing event based on the value assessment update signal in the new order data stream generated by the order acquisition module and the store operation data stream. Specifically, this includes:

[0048] S2.1: Perform multi-source heterogeneous data access processing on the value assessment update signals in the new order data stream and store operation data stream generated by the order collection module to obtain an initial business signal set containing the original transaction timestamp, product SKU identifier, trigger source store unique code, and instantaneous sales rate.

[0049] The S2.1 sub-step aims to standardize the access and preprocessing of multi-source heterogeneous business signals, providing a unified initial data set for subsequent value deviation calculation. This step is located at the beginning of the inventory rebalancing event generation and directly receives the data flow output from the order acquisition module and the store operation monitoring module.

[0050] The system receives real-time transaction log data from the order acquisition module and value assessment update signals from the store operation data stream. This data includes raw TCP / IP packet payloads, application-layer JSON messages, and binary protocol frames. The received multi-source heterogeneous data undergoes protocol parsing and unpacking to extract key fields, including a globally unique transaction ID, product SKU identifier, trigger source store unique code, transaction timestamp, and instantaneous sales quantity. For the timestamp data, time zone normalization and clock synchronization correction are performed, converting all local times to Coordinated Universal Time (UTC) to eliminate clock drift errors between distributed systems. Based on the corrected timestamps, a sliding time window method is used to calculate the instantaneous sales rate. The window length is set to 5 seconds, and the instantaneous rate index reflecting current sales activity is generated by calculating the ratio of cumulative sales within the window to the window duration. Dictionary mapping verification is performed on the extracted product SKU identifiers and store unique codes to ensure the data points to valid inventory management entities, removing noisy data containing illegal or delisted products. The cleaned transaction timestamps, SKU identifiers, store codes, and instantaneous sales rates are encapsulated into structured tuple objects to form the initial business signal set. Through the above-mentioned multi-source heterogeneous data access and standardized processing, the scattered and inconsistent original business flows are transformed into a set of initial business signals with spatiotemporal consistency, realizing the accurate capture and quantitative characterization of inventory change triggers, laying a data foundation for subsequent dynamic value assessment.

[0051] S2.2: Based on the initial business signal set, perform dynamic value deviation calculation operation, and use the preset store profit margin model and sales cycle decay function to perform weighted calculation on the instantaneous sales rate to generate a dynamic value deviation value that represents the deviation of the current store operating value from the baseline.

[0052] The system receives the initial business signal set generated by S2.1, which includes the original transaction timestamp, product SKU identifier, trigger source store unique code, and instantaneous sales rate. It extracts the average profit margin data and sales cycle benchmark value of the trigger source store within a preset historical time window to construct a basic parameter set for store operational value assessment. The instantaneous sales rate is normalized to eliminate the influence of different units of measurement on value assessment, generating a standardized real-time sales index.

[0053] Using a pre-defined store profit margin model, the standardized real-time sales index is weighted and multiplied with the historical average profit margin to calculate the theoretical expected revenue contribution at the current moment. A sales cycle decay function is introduced to calculate a time sensitivity coefficient based on the remaining time until the end of the sales cycle. This coefficient increases non-linearly as the remaining time decreases, reflecting the urgency of inventory nearing the end of the cycle.

[0054] The dynamic value deviation value is calculated using the following formula:

[0055]

[0056] in, For dynamic value deviation, This is the profit weighting coefficient. To standardize the real-time sales index, Historical average profit rate As a periodic adjustment factor, Based on remaining time The calculated exponential decay term.

[0057] Substitute the calculated theoretical expected revenue contribution value and time sensitivity coefficient into the above formula, and perform a subtraction operation to obtain a dynamic value deviation value that represents the current store's operational value deviating from the baseline. A positive value indicates that the current operational value is higher than the baseline, and inventory should be replenished first; a negative value indicates that it is lower than the baseline, and inventory should be considered for removal.

[0058] By performing dynamic value deviation calculation, the multi-source heterogeneous business signals from the previous step are transformed into quantified value deviation indicators, enabling accurate measurement of the real-time operational value of the store and providing core data support for subsequent timeliness constraint level determination.

[0059] S2.3: Execute the timeliness constraint level determination logic based on the dynamic value deviation value and the instantaneous sales rate in the initial business signal set. Map the continuous deviation value to discrete second-level response or minute-level optimization label through a multi-level threshold comparison method to generate a timeliness constraint level identifier that determines the urgency of inventory reallocation.

[0060] The system receives the dynamic value deviation value generated by S2.2 and the instantaneous sales rate from the initial business signal set acquired by S2.1, which serve as the input data source for determining the timeliness constraint level. A multi-level threshold comparison method model is constructed, setting three levels of timeliness response intervals: high, medium, and low, corresponding to three labels: second-level response, minute-level optimization, and conventional asynchronous processing, respectively.

[0061] The instantaneous sales rate is normalized to eliminate differences in sales volume dimensions across different SKUs, generating a standardized sales activity index. The dynamic value deviation value is then weighted and fused with the standardized sales activity index to calculate a comprehensive urgency score, quantifying the real-time demand intensity for inventory reallocation.

[0062] The overall urgency score is calculated using the following formula. :

[0063]

[0064] in, This represents the dynamic value deviation value. To standardize sales activity metrics, For value weighting coefficients, It is the sales volume weighting coefficient, and .

[0065] A first threshold and a second threshold are preset, where the first threshold is greater than the second threshold. The overall urgency score is compared with the first threshold. If the overall urgency score is greater than or equal to the first threshold, it is determined to be an extremely urgent scenario, and a "second-level response" label is generated, triggering the instant state transition logic of the memory snapshot chain.

[0066] If the second threshold is less than or equal to the overall urgency score but less than the first threshold, it is determined to be a moderately urgent scenario. A "minute-level optimization" label is generated, which allows multiple minor changes to be aggregated within a local time window before performing batch state merging to reduce system load.

[0067] If the overall urgency score is less than the second threshold, it is determined to be a low-urgency scenario. A "regular asynchronous" label is generated, and the event is placed in a low-priority queue for processing by the background scheduler during system idle periods to avoid resource contention.

[0068] By using a multi-level threshold comparison method, the continuous dynamic value deviation and sales rate are transformed into discrete time constraint level identifiers, enabling refined hierarchical control of inventory reallocation strategies and ensuring millisecond-level response capabilities in high-value, high-frequency change scenarios.

[0069] S2.4: Utilize the product SKU identifier, trigger source store unique code, dynamic value deviation value, and timeliness constraint level identifier to perform inventory rebalancing event encapsulation processing, and assemble the meta-information payload carrying causal tags according to the predefined event protocol format to generate a standardized inventory rebalancing event object.

[0070] The system receives the dynamic value deviation value and timeliness constraint level identifier generated in S2.3, as well as the product SKU identifier and trigger source store unique code extracted in S2.1. A standardized event payload data structure is constructed, defining a binary encapsulation template containing a header metadata area and a main payload area. The header metadata area is pre-allocated with a fixed byte length to store the event version number, generation timestamp, and causal tracking ID. The product SKU identifier is mapped to a globally unique integer index and filled into the resource identifier field of the main payload area. The trigger source store unique code is converted to UTF-8 encoding, its byte length is calculated, and written to a fixed-length string field. The dynamic value deviation value is serialized using the IEEE 754 double-precision floating-point standard to ensure consistency in cross-platform value parsing. The timeliness constraint level identifier is mapped to an enumerated integer value and written to the control flag field. A causal tagging mechanism is introduced, extracting the microsecond-level timestamp of the current system clock as proof of the event occurrence time. Combining the trigger source store ID and SKU index, a local checksum is calculated using the CRC32 algorithm to generate a causal association fingerprint. The causal fingerprint is written into the verification field of the header metadata area to ensure the traceability and integrity of the event source. The assembled binary data block is Base64 encoded and converted into an ASCII string format suitable for distributed message bus transmission. An event routing key is appended, which is composed of an SKU hash prefix and a timeliness level code, used to guide the message middleware's sharding strategy. Through the above encapsulation process, discrete business parameters are transformed into standardized inventory rebalancing event objects with strong consistency constraints and causal traceability capabilities, achieving highly reliable data transmission and millisecond-level routing distribution under an event-driven architecture.

[0071] For example, for a product with SKU-ID 10086, the triggering source store is Store_A, the dynamic value deviation is 0.85, and the timeliness level is second-level response. The system maps the SKU-ID to the integer 10086, and Store_A is encoded as "STORE_A_01". The deviation of 0.85 is serialized as hexadecimal 0x3FEAE147AE147AE1. The timeliness level is mapped to the enumeration value 1. The causal fingerprint is calculated based on the combination of the timestamp 1715668800123456 and the ID to obtain the CRC32 value 0xA1B2C3D4. The final generated Base64 encoded event string is 128 bytes long, and the routing key is "10086_L1". After the event object is injected into the bus, it is accurately routed to the high-frequency processing queue, with a parsing time of less than 0.1ms, ensuring real-time capture by the subsequent snapshot chain listener.

[0072] S2.5: Based on the standardized inventory rebalancing event object, perform a distributed event bus injection operation, and use a publish-subscribe mechanism to route the event object to the specified event-driven queue channel to form a pending event-driven queue that can be asynchronously captured by the memory snapshot chain listener.

[0073] The system receives standardized inventory rebalancing event objects generated by S2.4. These objects contain the target SKU identifier, triggering source store ID, dynamic value deviation, and timeliness constraint level. The system parses the timeliness constraint level identifier from the event object and maps it to the corresponding physical Topic channel in the distributed event bus according to a preset QoS routing policy. If the timeliness level is 'second-level response', it routes to a high-priority, low-latency channel; if it is 'minute-level optimization', it routes to a standard throughput channel. Through a channel isolation mechanism, it ensures that high-urgency events are not blocked by low-frequency batch events, achieving hierarchical transmission of the event stream.

[0074] The routed event objects are serialized and encoded using the Protobuf binary protocol to convert the event payload into a compact byte stream. A globally unique event tracking ID and causal timestamp are injected into the byte stream header to construct a message packet with end-to-end traceability. Zero-copy technology is used to write the serialized message packet directly to the kernel buffer, reducing the data copy overhead between user space and kernel space and lowering event injection latency.

[0075] The distributed event bus's publish interface is invoked to push message packets to the specified Topic asynchronously and non-blockingly. The event bus uses a consistent hashing algorithm to calculate a partition key based on the triggering source store ID, accurately distributing events to specific partition queues. This partitioning mechanism ensures that events related to the same store or SKU are processed in order, avoiding logical errors caused by concurrent conflicts. A memory snapshot chain listener subscribes to this partition queue, capturing pending events in real-time through long polling or push modes.

[0076] Through the aforementioned publish-subscribe mechanism and hierarchical routing strategy, standardized inventory rebalancing events are transformed into ordered, isolated, and low-latency event-driven queue data, enabling millisecond-level transparent transmission of events from the business generation end to the decision execution end, providing real-time input for subsequent local feasibility assessment.

[0077] Step S3: Construct a dynamic collaboration graph between stores, and based on this graph, perform local feasibility assessment and candidate allocation path enumeration for the inventory rebalancing event to generate a local feasibility assessment report. Specifically, this includes:

[0078] S3.1: Obtain historical transfer success rate, logistics timeliness decay factor and real-time service load data, and perform multi-source heterogeneous data alignment and time window aggregation processing on the historical transfer success rate, logistics timeliness decay factor and real-time service load data to generate an initial collaborative relationship matrix containing the basic attributes of store nodes and the original weights of edge connections.

[0079] Substep S3.1 aims to construct an initial collaborative relationship matrix, providing a structured data foundation for subsequent dynamic map generation. This step receives three types of heterogeneous input data: historical allocation success rate, logistics timeliness decay factor, and real-time service load data.

[0080] The historical transfer success rate is aggregated over a time window. The total number of transfer requests and the number of successful responses for each store node pair within a preset period are extracted to calculate the original success rate index, forming a static edge weight base value that reflects the stability of long-term cooperation.

[0081] Alignment processing is performed on the logistics timeliness decay factor. Based on the current system clock, the logistics time consumption records within the most recent N time slices are extracted. The moving average method is used to eliminate instantaneous fluctuation noise and generate a time-series feature vector representing the recent logistics smoothness.

[0082] Standardized sampling is performed on real-time service load data to obtain the current CPU utilization, memory usage, and network I / O latency of each store gateway. The Z-Score standardization method is used to eliminate the differences in units and map the multi-dimensional load indicators to a unified numerical range.

[0083] The static edge weights, time-series feature vectors, and standardized load metrics processed above are fused from multiple data sources. A two-dimensional matrix structure is constructed using store IDs as row and column indices. The matrix elements contain the original collaborative attribute data of the corresponding store pairs.

[0084] By using multi-source heterogeneous data alignment and time window aggregation, scattered historical transaction records, logistics logs, and real-time monitoring streams are transformed into a structured initial collaborative relationship matrix. This enables the initial quantification and data standardization of inter-store collaborative capabilities, providing a consistent data input format for subsequent dynamic weight calculations.

[0085] For example, the statistical period is set to the past 7 days, and the logistics time series window length is 24 hours. Store A has made 100 historical transfer requests to Store B, with 90 successful, resulting in a raw success rate of 0.9. The average logistics time from Store A to Store B in the past 24 hours is 12 hours, with a standard deviation of 2 hours. After moving average processing, the timeliness feature value is 12.5. Store B's current CPU utilization is 60%, and its memory usage is 70%, resulting in load metrics of 0.5 and 0.6 respectively after Z-score standardization. The above data is encapsulated into matrix elements M[A][B], containing the fields {success_rate: 0.9, latency_feat: 12.5, cpu_load: 0.5, mem_load: 0.6}. The same operation is performed on all store pairs to generate an initial collaboration relationship matrix of size P×P, where P is the total number of stores. This matrix fully preserves the original information of each dimension without introducing nonlinear transformations, ensuring that subsequent graph neural networks can learn complex collaborative patterns based on the original distribution, significantly improving the transparency and interpretability of data processing.

[0086] S3.2: Based on the initial collaborative relationship matrix, a dynamic graph neural network is used to perform probability mapping on the historical transfer success rate between store nodes, exponential weighting calculation on the logistics timeliness decay factor, and normalization and reverse correction on the real-time service load, so as to generate a dynamic collaborative graph between stores that represents the real-time dynamic collaborative strength between stores.

[0087] The system receives the initial collaborative relationship matrix generated by S3.1, which contains the original connection weights and basic attribute data between store nodes. It performs probability space mapping on the historical transfer success rate data in the matrix, using the Sigmoid activation function to transform discrete Success / Failure records into continuous probability values ​​between 0 and 1, constructing a static trust baseline reflecting long-term cooperation stability. For the logistics timeliness decay factor, a time decay exponential model is introduced. Using the current moment as a benchmark, historical logistics time consumption data is exponentially weighted to generate a timeliness sensitivity coefficient that dynamically changes over time, quantifying the impact weight of recent logistics fluctuations on collaborative efficiency. Real-time service load data is acquired, and Z-Score standardization is performed on multi-dimensional load indicators such as CPU utilization, memory usage, and I / O wait time to eliminate dimensional differences and form a normalized real-time load vector. Based on the normalized load vector, a reverse correction operation is performed. By calculating the reciprocal of the load value and applying a smoothing factor, a load suppression coefficient is generated. The suppression coefficient corresponding to high-load stores is significantly reduced, thereby weakening their priority as transfer targets in the graph. A dynamic graph neural network is employed, using static trust baselines, timeliness sensitivity coefficients, and load suppression coefficients as node feature inputs. A multilayer perceptron aggregates neighbor node information to update the edge weight matrix. A graph attention mechanism is used to dynamically allocate attention weights across different feature dimensions, automatically identifying the most influential collaborative factors in the current inventory rebalancing scenario and generating the final dynamic collaboration strength score between stores. Through this multi-source feature fusion and dynamic weighting process, the initial collaboration relationship matrix is ​​transformed into a dynamic collaboration graph representing the real-time dynamic collaboration strength between stores, achieving accurate quantification and real-time perception of store collaboration capabilities in complex operating environments.

[0088] For example, assuming a historical transfer success rate of 85% from store A to store B, a static trust value of 0.92 is obtained after Sigmoid mapping. The average logistics timeliness over the past 7 days is 24 hours, a 10% delay from the baseline, resulting in a timeliness sensitivity coefficient of 0.85 after exponential weighting. Store B's current CPU load is 80%, resulting in a load suppression coefficient of 0.4 after Z-Score standardization and reverse correction. A dynamic graph neural network aggregates these features, assigning an attention weight of 0.6 to the timeliness factor, a weight of 0.3 to the load factor, and a weight of 0.1 to the trust factor, calculating a dynamic collaboration strength of 0.78 from A to B. This value reflects in real time the decreased capacity of store B under high load, ensuring that subsequent path searches avoid congested nodes and significantly improving the actual fulfillment success rate of inventory transfers.

[0089] S3.3: Receive the inventory rebalancing events to be processed and the generated inter-store dynamic collaboration graph. Based on the graph traversal algorithm, search for connected subgraphs that meet the timeliness constraint level in the inter-store dynamic collaboration graph with the triggering source store ID as the starting node, so as to generate an initial candidate path space containing a set of potential transfer target stores.

[0090] The system receives pending inventory rebalancing events and the generated inter-store dynamic collaboration graph. It parses the triggering source store ID and time constraint level from the events, mapping them to the starting node identifier and maximum path depth threshold of the graph traversal algorithm. Based on a breadth-first search strategy, with the triggering source store ID as the root node, it explores adjacent store nodes layer by layer in the inter-store dynamic collaboration graph, constructing an initial search tree structure. During the traversal, it reads edge weight data in real time; this data represents the dynamic collaboration strength between stores and serves as the core criterion for path reachability. For each hop expansion operation, it calculates the cumulative collaboration cost of the current path and evaluates the path effectiveness using the following formula:

[0091]

[0092] in, Accumulate collaborative costs for the path. Let be the dynamic cooperative strength of the i-th jump edge. For the estimated delivery time of the i-th hop, The current traversal depth is set. The calculated cumulative collaborative cost of the path is compared with the preset timeliness constraint threshold of 5.0. If the cumulative collaborative cost of the path exceeds the threshold or the traversal depth reaches the maximum limit, the branch is pruned, and the downward exploration stops. All end nodes that meet the timeliness constraint and have not reached the depth limit are retained to form a set of potential transfer target stores. For each target store in the set, the complete path sequence from the source node to the target node and the corresponding cumulative collaborative cost are recorded to construct an initial candidate path space. Through the above graph traversal and dynamic pruning processing, the static graph generated in the previous step is transformed into a data set containing multiple potential transfer paths that meet the timeliness requirements. This achieves efficient screening and preliminary route planning for massive store nodes and significantly reduces the computational complexity of subsequent multi-dimensional constraint pruning.

[0093] S3.4: For each potential allocation path in the initial candidate path space, combined with the current value deviation information in the inventory rebalancing event, perform a multi-dimensional constraint pruning operation to eliminate invalid paths that cannot meet the dynamic value weight coefficient matching requirements, so as to generate an optimized list of feasible candidate allocation paths.

[0094] Receive the initial candidate path space generated by S3.3, which contains a set of potential transfer target stores that start from the triggering source store and meet the basic connectivity and timeliness constraints. Extract the current value deviation metadata from the inventory rebalancing event. This value represents the degree to which the operational value of the triggering source store deviates from the baseline due to sales fluctuations, and serves as the core dynamic constraint factor for path selection.

[0095] Traverse each potential transfer path in the initial candidate path space to obtain the real-time dynamic value weight coefficient of the target store at the path's endpoint. This coefficient is calculated by weighting the store's historical profit margin, current inventory turnover rate, and brand premium capability, reflecting the expected revenue conversion efficiency of the store after taking over the inventory.

[0096] A value matching evaluation model is constructed to calculate the coupling relationship between the value deviation of the triggering source store and the value weight coefficient of the target store. The path value matching score is calculated using the following formula:

[0097]

[0098] in, Path value matching score, The target store's dynamic value weighting coefficient. The dynamic coordination strength score, To trigger the current value weight of the source store, This is a value difference penalty factor.

[0099] Set multi-dimensional constraint pruning thresholds, including a minimum value matching score threshold of 0.5 and a maximum value deviation tolerance of ±0.15. Compare the calculated path value matching score with the minimum threshold, and eliminate inefficient paths with scores below the threshold to ensure that inventory flows to high-value conversion areas.

[0100] The remaining paths undergo a value deviation tolerance check, calculating the absolute difference between the value weights of the source store and the target store. If the difference exceeds the preset tolerance limit, the path is deemed to have a value mismatch risk and is eliminated to prevent long-term ecological imbalance caused by the one-way flow of resources from low-value stores to extremely high-value stores.

[0101] Perform priority sorting on the set of paths that have undergone pruning, arranging them from high to low according to the path value matching score, and generating an optimized list of feasible candidate allocation paths.

[0102] Through the above multi-dimensional constraint pruning operation, the initial candidate path space of the previous step is transformed into an enumeration list of feasible candidate allocation paths after value filtering and priority sorting, so as to achieve accurate selection of inventory redistribution paths in terms of economic benefits and significantly improve the value conversion efficiency of the overall supply chain.

[0103] S3.5: Based on the feasible candidate allocation path enumeration list, simulate the execution of a single inventory share transfer logic and pre-calculate the virtual inventory status indicators after the transfer. Compare and analyze the simulation results with the prefix consistency verification rules to generate a local feasibility determination report containing the prefix consistency verification results and the priority of the recommended path.

[0104] The system receives the enumerated list of feasible candidate allocation paths generated in S3.4, extracts the source store ID, target store ID, and the quantity of inventory to be transferred for each path, and uses these as input parameters for the virtual state simulation. It obtains the real-time available inventory share benchmark value for each store against the target SKU stored in the tail node of the current memory snapshot chain, and constructs an initial virtual inventory state vector. For each candidate path in the enumerated list, it performs a logical simulation operation for inventory share transfer, deducting the quantity to be transferred from the virtual inventory share of the source store and adding the same quantity to the virtual inventory share of the target store, generating intermediate inventory distribution data after the first state transition. Based on the intermediate inventory distribution data, combined with the dynamic value weight coefficient calculated in S2.2, it uses a weighted summation method to pre-calculate the global virtual inventory value index after the transfer, quantifying the potential contribution of this transfer to the overall operational value. Using a prefix consistency check rule, it compares the pre-calculated global virtual inventory value index with the value deviation threshold carried in the inventory rebalancing event, and simultaneously verifies whether the intermediate inventory distribution meets the non-negativity constraint and the maximum carrying capacity limit of the store. If the verification passes, the candidate path is marked as a valid and feasible path, and its corresponding virtual inventory status index and value gain score are recorded. If the verification fails, the path is marked as invalid and removed. After traversing all candidate paths, the valid and feasible paths are sorted in descending order according to the value gain score, generating a local feasibility judgment report that includes the priority ranking of recommended paths. Through the above simulation execution and verification comparison process, the candidate path list is transformed into a judgment report containing the prefix consistency verification results and recommended priorities, realizing rapid pre-playing and optimization of inventory redistribution schemes in memory, ensuring the legality and value maximization of subsequent state transitions.

[0105] For example, a candidate path is set as transferring 10 items from store A to store B. The current snapshot shows that store A has 50 items in stock and store B has 5 items. After the simulated deduction, store A's virtual inventory becomes 40 items, and store B's becomes 15 items. Assume store A's value weight is 1.2 and store B's is 1.8. The pre-calculated global virtual value change is 6.0. The validation of non-negativity constraints (40>0, 15>0) and capacity limits (15<100) both pass. This path is marked as valid, and the value gain of 6.0 is included in the report. If another path results in store A's inventory being -5, the validation fails and the path is removed. The final report is sorted by gain score to guide the execution of step S4.

[0106] Step S4: Based on the local feasibility assessment report, perform a prefix consistency check. If the check passes, generate an atomic new snapshot node; if the check fails, generate a temporary fulfillment instruction for the local store buffer pool. Specifically, this includes:

[0107] S4.1: Obtain the difference vector between the candidate allocation path enumeration results in the local feasibility judgment report and the tail node of the current memory snapshot chain. Perform state transition simulation calculation on the candidate allocation path enumeration results based on the prefix consistency verification method to generate first-level intermediate state snapshot data containing the virtual inventory share and virtual value weight coefficient after the first state transition.

[0108] The S4.1 sub-step aims to perform the first state transition simulation based on the candidate paths in the local feasibility assessment report, generate first-level intermediate state snapshot data, and provide a benchmark for subsequent secondary simulations.

[0109] Obtain the list of feasible candidate allocation paths after multi-dimensional constraint pruning from the local feasibility assessment report, extract the source store ID, target store ID and the quantity of inventory to be transferred for each path, and construct the initial state transition instruction set.

[0110] Locate the tail node of the current memory snapshot chain, read the difference vector stored at that node, and use the incremental merging method to restore the baseline of the real-time available inventory share for each store for the target SKU, forming a global inventory status view at the current moment.

[0111] For the source store in the candidate path, extract its current available inventory share from the global inventory status view, perform non-negativity verification and compare it with the minimum safety stock threshold. If the current share is lower than the sum of the quantity to be transferred and the safety stock, mark the path as infeasible and remove it.

[0112] For paths that pass verification, calculate the remaining share after deducting the source store's inventory. At the same time, based on the logistics timeliness decay factor in the dynamic collaboration graph between stores, calculate the expected increase in the target store's available share after receiving the transferred inventory.

[0113] A dynamic value weighting coefficient correction mechanism is introduced. Based on the current value deviation in the inventory rebalancing event, the expected available share of the target store is adjusted in a weighted manner to reflect the priority absorption effect of high-value stores on inventory resources.

[0114] Based on the calculated virtual inventory share, combined with the preset profit margin model, the expected revenue contribution of each store under the current allocation plan is re-estimated, and first-level intermediate snapshot data containing virtual inventory share and virtual value weight coefficient is generated.

[0115] Through the above state transition simulation calculation, the static candidate path is transformed into intermediate data containing dynamic value assessment results, which realizes the preliminary quantitative verification of the feasibility of the inventory redistribution scheme and significantly reduces the probability of invalid paths entering the secondary deduction.

[0116] S4.2: Using the virtual inventory share and virtual value weight coefficient in the first-level intermediate snapshot data, combined with the timeliness constraint level of the inventory rebalancing event, perform a second-level state transition deduction process to generate second-level intermediate snapshot data containing the final virtual inventory share and final virtual value weight coefficient after the second state transition.

[0117] Receive the first-level intermediate state snapshot data generated by S4.1, extract the virtual inventory share vector and the initially adjusted virtual value weight coefficient contained therein, and use them as the initial input conditions for the second-order state transition deduction.

[0118] Based on the timeliness constraint level in inventory rebalancing events, the maximum allowable state transition time window threshold and the upper limit of value deviation tolerance are analyzed, and a set of boundary constraints for verifying the legality of secondary transitions is constructed.

[0119] By utilizing the edge weight data of candidate paths in the dynamic collaboration graph between stores, combined with the logistics timeliness decay factor, the estimated physical transfer time from the triggering source store to the target store is calculated, and time-dimensional feasibility verification parameters are generated.

[0120] An exponential smoothing forecasting model is used to extrapolate the short-term fluctuation trend of the virtual inventory share under the first intermediate state, eliminate instantaneous order noise interference, and generate a corrected inventory vector that reflects the inventory stability within a small future time slice.

[0121] By substituting the modified inventory vector into the dynamic value assessment function, the theoretical operating value of each store at the expected moment of the second leap is recalculated. This function introduces a sales cycle decay term to quantify the non-linear growth relationship of inventory holding costs over time.

[0122] By comparing the calculated theoretical operational value with the value gain threshold set by the time constraint level, it can be determined whether the secondary state transition can achieve the expected value enhancement under the premise of meeting the time requirement.

[0123] If the value gain meets the threshold and the estimated time is within the time window, then the current corrected inventory vector and theoretical operating value are locked and encapsulated as second-level intermediate snapshot data; otherwise, the candidate path is marked as infeasible and the deduction of the current path is terminated.

[0124] Through the above-mentioned secondary state transition deduction process, the preliminary simulation results of the previous step are transformed into second-level intermediate state snapshot data that have been verified by both the time dimension and the value dimension, thus realizing the accurate feasibility verification of the inventory redistribution scheme under millisecond-level response constraints.

[0125] S4.3: Based on the final virtual inventory share and final virtual value weight coefficient in the second-level intermediate snapshot data, a threshold comparison is performed against the timeliness constraint level of the inventory rebalancing event. If the timeliness constraint level is met, an atomic submission flag is generated. If the timeliness constraint level is not met, a temporary fulfillment instruction for the local store buffer pool and an asynchronous compensation queue transfer flag are generated.

[0126] S4.4: For the generated atomic commit token, extract the difference vector between the version hash of the current memory snapshot chain tail node and the second-level intermediate snapshot data, and perform an immutable snapshot node encapsulation operation to generate an atomic new snapshot node carrying complete causal chain information.

[0127] Obtain the atomic commit flag generated in step S4.3. This flag indicates that the current inventory rebalancing event has passed the prefix consistency check and meets the conditions for state transition. Extract the version hash value stored in the tail node of the current memory snapshot chain. This hash value serves as the unique digital fingerprint of the previous state, used to ensure the causal continuity of the new node in the chain structure. Read the second-level intermediate snapshot data calculated in step S4.2, and parse out the final virtual inventory share vector and the final virtual value weight coefficient vector after two state transitions. Using differential encoding, perform element-wise subtraction between the final virtual inventory share vector in the second-level intermediate snapshot data and the baseline inventory state represented by the tail node of the current memory snapshot chain, generating a lightweight difference vector containing only the net change. Perform sparsification on the generated lightweight difference vector, removing dimensions with zero values, and retaining only the store-SKU combinations that have undergone actual inventory changes or value weight adjustments and their corresponding change values ​​to minimize storage space usage. The SHA-256 cryptographic hash algorithm is used to concatenate the lightweight difference vector, the version hash value of the previous node, and the high-precision timestamp of the current system into the original data string. A one-way hash operation is performed on the original data string to generate a new version hash value, which serves as both a unique identifier for the new snapshot node and a tamper-proof verification code. An immutable snapshot node data structure is constructed, encapsulating the lightweight difference vector, the newly generated version hash value, the reference to the version hash value of the previous node, and the event causal marker metadata into independent objects. Using a CAS atomic operation instruction, the memory address pointer of the new snapshot node is written to the next pointer field of the current tail node, completing the logical link. Through the above immutable snapshot node encapsulation process, the verification result of the previous step is transformed into an atomic new snapshot node carrying complete causal chain information, achieving the expected technical effect of millisecond-level persistence and traceability of inventory status changes.

[0128] S4.5: For the generated local store buffer pool temporary fulfillment instructions and asynchronous compensation queue transfer flags, associate the trigger source store ID with the target SKU identifier, and perform the downgrade processing state initialization configuration to form an asynchronous compensation queue transfer flag state waiting for the background scheduler to perform global rebalancing during off-peak hours.

[0129] Upon receiving a temporary fulfillment instruction for the local store buffer pool and an asynchronous compensation queue transfer flag generated after a prefix consistency check failure, the unique code of the triggering source store and the target product SKU identifier encapsulated in the instruction are extracted. A hash index lookup is performed on the extracted unique code of the triggering source store to locate the corresponding store context object in memory, obtaining the store's current operational status identifier and buffer pool access permission configuration. Based on the target product SKU identifier, the associated local buffer pool handle is retrieved from the inventory mapping table of the store context object to verify whether the current available capacity of the buffer pool meets the temporary deduction threshold requirement. If the verification passes, a context object for the degraded event, containing the original event timestamp, degraded reason code, and expected compensation priority, is constructed and serialized into a binary data block. The persistent write interface of the distributed message middleware is called to inject the serialized data block into the tail node of the asynchronous compensation queue, generating a suspended compensation task sequence with a globally unique ID. The global degraded processing status flag of the inventory coordination module is updated, recording the start timestamp and the list of stores involved in this degraded operation, forming a final-state degraded processing closed loop awaiting the background scheduler to perform global rebalancing during off-peak hours. By using the above processing method, the verification failure result of the previous step is transformed into traceable asynchronous compensation task data, thereby ensuring business continuity and eventual data consistency in high-concurrency scenarios.

[0130] Step S5: Append the atomized new snapshot node to the end of the memory snapshot chain to complete the state transition, and broadcast the snapshot commit notification to the relevant store gateway based on the updated memory snapshot chain, thereby forming a closed loop for cross-store inventory redistribution. Specifically, this includes:

[0131] S5.1: Obtain the atomic new snapshot node containing the difference vector and version hash generated in the previous steps, as well as the tail node pointer of the current memory snapshot chain. Use memory address offset calculation technology to perform an atomic update operation on the tail node pointer, and write the physical memory address of the atomic new snapshot node into the reference field of the next node pointed to by the tail node pointer to generate an updated memory snapshot chain head and tail index structure pointing to the latest state.

[0132] Receive the atomic new snapshot node generated in the preceding step S4.4. This node contains a lightweight difference vector and a version hash value. Obtain the physical memory address pointer of the tail node of the current memory snapshot chain. This pointer points to the reference field of the last valid snapshot node in the chain.

[0133] By using memory address offset calculation techniques, the specific storage location of the tail node pointer in heap memory is determined. An atomic compare-and-swap (CAS) operation is performed on the tail node pointer, writing the physical memory address of the atomically generated new snapshot node into the reference field of the next node pointed to by the tail node pointer. This operation ensures that in a high-concurrency environment, the link to a new node can only be successfully established if the tail node has not been modified by other threads, thus guaranteeing the thread safety of the linked list structure.

[0134] If the CAS operation succeeds, the head and tail index structure of the memory snapshot chain is updated, and the tail node pointer is set to point to the newly generated atomic snapshot node. If the CAS operation fails, it indicates a concurrent write conflict, and the system automatically retryes reading the latest tail node and re-executes the linking operation until the linking succeeds. Through the above atomic pointer update mechanism, the state change data generated in the previous step is transformed into a structured extension of the memory snapshot chain, achieving millisecond-level state transitions and data consistency guarantees in a lock-free environment.

[0135] For example, for a product with SKU 'EL-2024-X', the current memory snapshot chain tail node address is 0x7F3A1B, and the newly generated atomic snapshot node address is 0x7F3A2C. The system performs a CAS operation to check if the next pointer at 0x7F3A1B still points to NULL or the expected value. If they match, 0x7F3A2C is written to that location. Assuming another thread attempts to append a node simultaneously in a concurrent scenario, and the CAS operation fails on the first attempt, the system retryes within 5 microseconds, reads the updated tail node address 0x7F3A2C, and links the new node 0x7F3A3D to it. Ultimately, the memory snapshot chain length increases by 1, the tail pointer stably points to the latest node, and the entire linking process takes less than 1 millisecond, significantly improving the efficiency of inventory status updates under high concurrency.

[0136] S5.2: Based on the updated memory snapshot chain head and tail index structure, read the difference vector data in the latest appended atomic new snapshot node, and use the incremental state merging method to accumulate the change value of available inventory share of each store carried in the difference vector with the baseline inventory status in the application layer cache to generate the latest full inventory status view reflecting the global inventory distribution at the current moment.

[0137] Input the head and tail index structure of the memory snapshot chain pointing to the latest state generated in the previous step S5.1, and the previous global inventory baseline state matrix stored in the application layer cache.

[0138] From the physical memory address pointed to by the updated memory snapshot chain tail node pointer, the data payload of the atomic new snapshot node is directly read, and the encapsulated lightweight difference vector is parsed out. This difference vector contains the list of target store IDs involved in this state transition and their corresponding changes in inventory share and value weight coefficient.

[0139] Access the application-layer local memory cache area, and based on the target store ID list in the difference vector, quickly locate and extract the baseline inventory status data of each corresponding store at the current moment through hash addressing, including the baseline available inventory share. Weighting coefficients of benchmark value .

[0140] An element-wise incremental merge operation is performed on the extracted baseline state data and the difference vector. For each affected store j, the updated real-time inventory status is calculated using the following formula:

[0141]

[0142]

[0143] in, The updated available inventory share for store j. These are the inventory change values ​​recorded in the difference vector; The updated value weight coefficient for store j. These are the weight changes recorded in the difference vector.

[0144] The calculated latest inventory status data for each store is then populated back into the corresponding memory address space of the application layer cache, overwriting the original baseline status data to form a memory-consistent latest full inventory status view. This view serves as the data source for subsequent broadcast notifications, ensuring that the status information received by downstream gateways is strictly synchronized with the logical state of the memory snapshot chain.

[0145] By using incremental state merging, the atomic difference data from the previous step is transformed into full state indicators that can be accessed instantly by the application layer, achieving the expected technical effect of millisecond-level global inventory view refresh and low-latency state synchronization.

[0146] S5.3: Utilize the latest full inventory status view to extract the list of target store identifiers and corresponding inventory change summary information involved in this state transition. Combine this with a predefined event serialization protocol to perform binary encoding on the list of target store identifiers and inventory change summary information to generate a standardized snapshot submission notification message carrying the complete state change context.

[0147] Extract the target store identifier list and corresponding inventory change summary information involved in this state transition from the latest full inventory status view generated by S5.2, as the basic data source for message construction. Traverse the target store identifier list, and for each target store, retrieve its latest available inventory share value and dynamic value weight coefficient in the full inventory status view, forming a set of state change tuples at the store level. Based on the predefined event serialization protocol specification, initialize the binary encoding buffer, setting the byte order to big-endian mode to ensure cross-platform parsing consistency. Perform fixed-length hash mapping on the unique code of each store in the target store identifier list, converting it into a fixed-length binary identifier to eliminate the parsing overhead caused by variable-length strings. Perform fixed-point compression encoding on the inventory share change value in the inventory change summary information, retaining four decimal places of precision, converting floating-point values ​​into compact integer byte sequences, significantly reducing network transmission load. Normalize the dynamic value weight coefficient to the 0-1 range, and use single-precision floating-point format for binary serialization to ensure lossless transmission of value assessment data. Following the field order specified in the protocol, the version hash value, timestamp, store binary identifier, inventory change integer value, and value weight floating-point number are sequentially written into the encoding buffer to construct a structured binary data block. Cyclic redundancy check (CRC) calculations are performed on the constructed binary data block, generating a checksum which is appended to the end of the message to ensure data integrity during distributed network transmission. Through this binary encoding process, the full inventory status view from the previous step is transformed into a standardized snapshot commit notification message carrying the complete status change context, achieving the expected high-efficiency cross-store status synchronization with millisecond-level low latency.

[0148] For example, for a product with SKU 'EL-2024-X', the target store identifier list includes store A, store B, and store C. Store A increases its inventory by 10 units, with a value weight of 0.85; store B decreases its inventory by 10 units, with a value weight of 0.92. The system maps store ID 'A001' to a 4-byte binary number 0x000003E9, the inventory change value of 10 to a 2-byte integer 0x000A, and the value weight of 0.85 to a 4-byte floating-point number 0x3F59999A. The resulting standardized snapshot submission notification message is 64 bytes long, an 85% reduction compared to JSON format, and network transmission time is reduced to less than 0.5 milliseconds, significantly improving collaborative response speed.

[0149] S5.4: Based on the standardized snapshot commit notification message, the publish / subscribe interface of the distributed message middleware is called. The standardized snapshot commit notification message is pushed concurrently to the receiving queue of all store gateways corresponding to the target store identifier list through a multicast routing strategy, so as to trigger each store gateway to perform local inventory data synchronization update operation and form a collaborative response result with consistent inventory status across stores.

[0150] The system receives standardized snapshot commit notification messages generated in previous steps, parses the target store identifier list and corresponding inventory change summary information contained in the message, and extracts the unique routing address and message priority tag for each target store gateway. Based on the publish-subscribe interface of the distributed message middleware, a multicast routing channel is constructed for the target store identifier list, and the standardized snapshot commit notification message is replicated into an independent message copy equal to the number of target stores. Zero-copy technology is used to directly map the message copies to the kernel buffer, avoiding redundant data copying between user space and kernel space, and reducing CPU load and memory bandwidth consumption. According to the multicast routing strategy, the current network latency weight and service load index of each target store gateway are calculated, and the scheduling order of the message sending queue is dynamically adjusted to ensure that inventory update requests from high-priority stores are transmitted first. Message copies are concurrently pushed to the receiving queue of each target store gateway through a non-blocking I / O model, and an asynchronous callback mechanism is used to monitor the message delivery status, achieving millisecond-level cross-store inventory status synchronization notification. By using the multicast routing and concurrent push processing methods described above, the standardized notification messages generated in the previous step are transformed into local inventory update instructions that can be processed in real time by each store gateway, thereby achieving consistent and coordinated response to inventory status across stores, significantly reducing broadcast latency and improving system throughput.

[0151] For example, the standardized snapshot release notification message includes the identifiers of target stores A, B, and C, and the inventory change amounts for SKU 'EL-2024-X' (A:+10, B:-5, C:-5). The system parses the three target gateway addresses and constructs a multicast channel. Assume that store A has a network latency weight of 0.8, B 0.5, and C 0.9, with service load indices of 0.2, 0.4, and 0.3 respectively. The system determines the sending order as B, A, C based on a weighted algorithm. Zero-copy technology is used to map three message copies to the kernel buffer, and they are pushed concurrently via non-blocking I / O. Within 5ms, all gateway receiving queues receive message copies, triggering local inventory updates. Compared to traditional serial broadcasting, this parallel multicast mechanism reduces the average broadcast latency from 15ms to 5ms, significantly improving inventory synchronization efficiency in high-concurrency scenarios.

[0152] S5.5: Obtain the asynchronous confirmation receipt signal returned by each store gateway, count the number of store gateways that successfully received the standardized snapshot submission notification message and compare it with the length of the target store identifier list. If the comparison is consistent, mark the current state transition as completed and record the final submission timestamp to form a millisecond-level cross-store inventory reallocation closed-loop execution log with complete audit traceability capabilities.

[0153] Step S6: For temporary fulfillment instructions in the local store buffer pool, activate the corresponding store's local store buffer pool to perform a temporary inventory deduction operation. Specifically, this includes:

[0154] S6.1: Obtain the temporary fulfillment instruction and asynchronous compensation queue transfer mark generated in the local store buffer pool during the prefix consistency verification operation, extract the trigger source store unique code and target product SKU identifier contained therein, and perform association mapping parsing processing on the trigger source store unique code and target product SKU identifier to generate a context object of the event to be downgraded with a clear direction.

[0155] The system receives temporary fulfillment instructions and asynchronous compensation queue transfer flags triggered by prefix consistency check failures in the local store buffer pool. It parses the binary data stream in the instruction payload, extracting the unique code field of the triggering source store and the target product SKU identifier field. A hash index lookup is performed on the extracted unique code of the triggering source store to locate the corresponding store metadata record in the distributed cache cluster, verifying the store's activity and write permissions. Simultaneously, it reads the basic attribute information associated with the target product SKU identifier, including product category, specifications, and inventory unit conversion coefficients, ensuring data semantic consistency in subsequent buffer pool operations. The unique code of the triggering source store and the target product SKU identifier are structurally encapsulated to construct a context object for the degrading event, containing an event timestamp, a snapshot of the original value deviation, and a degradation reason code. This context object serves as the unique data carrier, running throughout the entire buffer pool deduction and asynchronous compensation process, ensuring data integrity and traceability in the degradation processing chain. Through the above parsing and encapsulation, the scattered verification failure signals are transformed into a context object for the degrading event with a clear business orientation, achieving seamless connection from status determination to degradation execution and ensuring the robustness of the inventory collaboration system in high-concurrency scenarios.

[0156] S6.2: Based on the unique code of the triggering source store in the context object of the event to be downgraded, locate and access the local store buffer pool storage area of ​​the corresponding store, read the available emergency inventory share data pre-stored in the local store buffer pool, and perform atomic deduction operation on the available emergency inventory share data to generate an updated buffer pool inventory level value that reflects the temporary fulfillment status.

[0157] Parse the context object of the event to be downgraded, extract the unique code of the triggering source store as the memory addressing key, and quickly locate the local store buffer pool shared memory segment address space of the store through hash index.

[0158] Within the located shared memory segment, the offset is calculated based on the target product SKU identifier, and the available emergency inventory share value stored at the current moment is directly read. This value represents the upper limit of backup inventory resources that the store can independently control in the event of a cross-store transfer failure.

[0159] Obtain the quantity to be deducted for this order carried in the inventory rebalancing event, construct atomic deduction logic, and use Compare-and-Swap (CAS) instructions to ensure exclusive modification of the buffer pool inventory value in a high-concurrency read-write environment, preventing abnormal water level caused by data competition.

[0160] Perform a deduction operation, subtract the order deduction quantity from the read available emergency inventory share value to obtain the remaining inventory value after temporary fulfillment. If the remaining value is less than zero, it is determined that the buffer pool is exhausted and the circuit breaker protection is triggered; otherwise, the remaining value is written back to the original memory address.

[0161] Record the high-precision timestamp of the deduction operation completion and the final written inventory value to form the updated buffer pool inventory level value reflecting the temporary performance completion status, which serves as the basic data source for the subsequent generation of performance vouchers.

[0162] By using the above-mentioned processing method based on shared memory addressing and CAS atomic instructions, the degradation instructions generated in the previous step are transformed into specific buffer pool inventory change data, thereby achieving emergency inventory locking within millisecond latency and ensuring the expected technical effect of data consistency and business continuity under high concurrency.

[0163] S6.3: Using the updated buffer pool inventory level value and the target product SKU identifier in the context object of the event to be downgraded, construct a local performance confirmation voucher containing temporary deduction amount, deduction timestamp and original event metadata, and perform serialization encapsulation operation on the local performance confirmation voucher to generate a standardized temporary performance record data packet for audit trail.

[0164] S6.4: Receive the standardized temporary performance record data packet and the asynchronous compensation queue transfer flag obtained in the previous steps, call the persistent write interface of the distributed message middleware, and inject the inventory rebalancing event load carrying complete causal chain information into the tail node of the asynchronous compensation queue to generate a suspended compensation task sequence waiting for the background scheduler to schedule.

[0165] Receive the standardized temporary fulfillment record data packet and asynchronous compensation queue transfer flag generated in the preceding steps, and parse the trigger source store unique code, target product SKU identifier, temporary deduction amount, and deduction timestamp metadata in the data packet. Extract the original inventory rebalancing event load, which includes the initial value deviation, timeliness constraint level, and candidate allocation path enumeration list to ensure that the degradation processing has complete causal traceability information.

[0166] The persistent write interface of the distributed message middleware is invoked to initialize the transaction context of the asynchronous compensation queue. A write-ahead logging (WAL) mechanism is used to serialize the inventory rebalancing event payload to be enqueued into a binary byte stream to prevent event loss due to system crashes.

[0167] Calculate the physical memory address offset of the tail node of the current asynchronous compensation queue and locate the write position based on the circular buffer structure. Perform a CRC32 checksum generation operation on the serialized event payload and append the checksum to the end of the payload to form an event data block with integrity protection.

[0168] The event data block carrying complete causal chain information is atomically written to the tail node of the asynchronous compensation queue. The tail pointer index in the queue metadata is updated to point to the next available position of the newly written node, and the queue length counter is incremented to ensure write consistency in a multi-threaded environment.

[0169] Listen for the persistence success signal returned by the underlying storage engine to confirm that the event data blocks have been synchronized to the disk or high-availability replica. Generate a sequence identifier for a suspended compensation task awaiting scheduling by the background scheduler. This identifier is associated with the original event ID and the local fulfillment record ID, forming a closed-loop degradation processing index.

[0170] By using the above persistent writing and atomic update processing methods, the temporary fulfillment results of the previous step are transformed into a reliably recoverable suspended state compensation task sequence, thereby achieving the technical effect of ensuring eventual consistency and business continuity in cross-store inventory redistribution under abnormal scenarios.

[0171] For example, for a product with SKU 'EL-2024-X', the triggering source store is store A. The standardized temporary fulfillment record data packet contains a deduction of 5 items and a timestamp of 1715668800123456 microseconds. The system calls the Kafka message middleware interface to serialize the event payload, which includes store A's code, SKU identifier, and original value deviation of 0.85, into a 256-byte binary stream. The CRC32 checksum is calculated as 0x8A3B2C1D and appended to the end of the payload. The asynchronous compensation queue tail offset Offset=10240 is located and atomically written to the data block. The tail pointer is updated to Offset=10241, and the queue length is increased from 100 to 101. After receiving the persistence success ACK signal, a suspended task ID 'TASK_COMP_9988' is generated and associated with the local fulfillment record 'REC_TEMP_7766'. This process ensures that in the event of a network partition or primary database failure, the background scheduler can accurately recover from the asynchronous queue and retry the global rebalancing task, significantly improving the system's fault tolerance.

[0172] S6.5: Based on the enqueue status signal of the suspended compensation task sequence, update the global degradation processing status flag of the current inventory collaboration module to mark that the current cross-store inventory redistribution process has entered the asynchronous compensation stage, so as to form a final state degradation processing closed loop that is temporarily supported by the local store buffer pool and waits for the global rebalancing to be performed during the off-peak period.

[0173] Receive the queue status signal of the suspended compensation task sequence generated in the previous steps. This signal contains the unique identifier of the asynchronous compensation queue, the event injection timestamp, and the current depth index of the queue.

[0174] The queued status signal is parsed to extract the trigger source store ID and target SKU identifier, and a global degradation processing context index key to be updated is constructed.

[0175] Based on the index key, access the shared memory status register of the inventory coordination module and read the initial value of the current global degradation processing status flag.

[0176] The atomic compare-and-swap (CAS) instruction is used to update the global degradation processing status flag from the "normal coordination" state to the "asynchronous compensation" state, ensuring the atomicity and thread safety of state changes in a high-concurrency environment.

[0177] Record the system's high-precision clock timestamp when the state change occurs, as a starting time marker for the degradation processing phase.

[0178] The updated status flags, start time markers, and associated compensation task sequence IDs are integrated to generate a global degradation processing status change log entry.

[0179] By using the above-mentioned atomic state update and log recording processing methods, the asynchronous enqueue action of the previous step is transformed into a system-level global degradation state identifier, realizing a smooth switch from real-time synchronous mode to asynchronous eventual consistency mode in the cross-store inventory redistribution process, ensuring the expected technical effects of business continuity and data traceability in scenarios where response latency exceeds the standard.

[0180] For example, for a product with SKU 'EL-2024-X', a successful enqueueing of the asynchronous compensation queue returns a status code of 200. The system reads the global status flag from shared memory, with an initial value of 0x00 (representing normal). A CAS operation is performed, with the expected value being 0x00 and the new value being 0x01 (representing asynchronous compensation in progress). If the CAS operation succeeds, a timestamp of 1715668800123456 microseconds is recorded. If other threads attempt to modify the status simultaneously, the CAS operation will retry if it fails until successful. Eventually, the global status flag stabilizes at 0x01, and the log records "State transitioned to asynchronous compensation stage," ensuring that the subsequent background scheduler can accurately identify and process suspended tasks, avoiding the risk of data inconsistency caused by state competition.

[0181] Step S7: Dynamically manage the length of the memory snapshot chain, and persist old snapshot nodes that exceed a preset change threshold to the local SSD storage medium via memory-mapped files. Specifically, this includes:

[0182] S7.1: Obtain the full snapshot node sequence in the current memory snapshot chain and the preset change count threshold parameter of 300 times. Based on the sliding window control strategy, perform time-series traversal and index comparison processing on the full snapshot node sequence to generate a candidate set of expired nodes containing the identifiers of old snapshot nodes to be archived.

[0183] S7.2: Read the identifiers of the old snapshot nodes to be archived from the candidate set of expired nodes, extract the corresponding difference vector data blocks and version hash metadata from the memory snapshot chain, and perform serialization encoding conversion processing to generate a standardized persistent data stream that meets the writing format requirements of the local SSD storage medium.

[0184] The S7.2 sub-step aims to transform the aging snapshot nodes awaiting archiving in memory into a standardized data stream suitable for SSD persistent storage, supporting subsequent low-latency writes and rapid recovery. It reads the identifiers of the aging snapshot nodes awaiting archiving from the candidate set of expired nodes and locates the corresponding physical memory address pointers in the hash index table of the memory snapshot chain based on these identifiers. It directly accesses the heap memory region where the node resides through the memory address pointers and extracts a lightweight difference vector data block. This data block contains the incremental change values ​​of each store's inventory share and the offset of the dynamic value weight coefficient. It performs a byte order alignment check on the extracted difference vector data block to ensure the storage consistency of multi-byte values ​​across different architectures, preventing data parsing errors during cross-platform recovery. It extracts the version hash metadata bound to the node in parallel, including the hash value of the previous node, the timestamp of the current node, and the causal event ID, forming a complete status verification header. It uses the Protocol Buffers serialization protocol to structure and encode the difference vector data block and version hash metadata, defining a fixed message description file to establish the field order and data type. During the encoding process, ZigZag variable-length integer encoding is used for sparse inventory increment values, compressing small-fluctuation inventory change values ​​into fewer bytes, significantly reducing I / O load. The timestamps in the version hash metadata are differentiated by relative time difference, storing only millisecond-level offsets relative to the base time point, further reducing metadata volume. The encoded binary data stream is assembled into a standard frame structure containing a length prefix, checksum, and payload, forming a standardized persistent data stream that conforms to the sequential write characteristics of local SSD media. Through the above serialization and compression processing methods, the stale node memory objects identified in the previous step are transformed into a compact binary persistent data stream, achieving ultimate optimization of storage space and ensuring data integrity during power outage recovery.

[0185] For example, for a product with SKU "ELEC-001", the candidate set of expired nodes includes five stale nodes with node IDs from "SN-20231027-005" to "SN-20231027-009". The system reads the memory pointer of node "SN-20231027-005", extracts the difference vector, where the inventory increment of store A is -2, the inventory increment of store B is +2, and the value weight coefficient offset is 0.05. ZigZag encoding is used to convert the integer -2 to 3 and +2 to 4, requiring only 1 byte to store each integer. The timestamp "1698374400123" in the version hash metadata has an offset of 123ms relative to the base time "1698374400000", and is encoded as a short integer. The data was serialized using Protocol Buffers into a 24-byte binary frame, containing a 4-byte header, a 4-byte CRC32 checksum, and a 16-byte payload. The resulting standardized persistent data stream was 85% smaller than the original JSON format, significantly improving SSD write throughput.

[0186] S7.3: Call the memory-mapped file interface provided by the operating system kernel to establish a mapping relationship between the standardized persistent data stream and the specified physical address space in the local SSD storage medium, and perform zero-copy direct write operation to generate a persistent memory snapshot chain archive file containing complete state history information.

[0187] Receive the standardized persistent data stream generated by the preceding steps. This data stream contains difference vector data blocks and version hash metadata of the old snapshot nodes to be archived, and serves as the input source for the memory-mapped write operation.

[0188] The mmap system interface provided by the operating system kernel is invoked to specify the pre-allocated file descriptor and starting physical page address in the local SSD storage medium, thereby establishing a bidirectional mapping channel between the user-mode virtual memory space and the kernel-mode file page cache.

[0189] Based on the established mapping relationship, a zero-copy direct write operation is performed, which directly maps the binary byte sequence in the standardized persistent data stream to the physical address space corresponding to the SSD, avoiding the overhead of multiple memory copying from the user buffer to the kernel buffer in the traditional I / O path.

[0190] During the write process, non-temporal store instructions supported by the CPU instruction set are used to bypass the L1 / L2 cache levels and directly write the data stream to the last level cache or pass it directly to the storage controller, thereby reducing cache pollution and improving write throughput.

[0191] Based on the physical characteristics of SSD media, a 4KB aligned page write strategy is implemented to ensure that the size of the data block written each time is consistent with the granularity of the SSD flash page, thereby reducing write amplification and extending the lifespan of the storage media.

[0192] By forcibly flushing the processor write buffer using the memory barrier instruction, all mapped write operations are logically completed atomically, generating a persistent memory snapshot chain archive file with complete state history information.

[0193] By using the zero-copy memory mapping write processing method described above, the standardized persistent data stream from the previous step is transformed into a persistent memory snapshot chain archive file physically stored on the local SSD, achieving the expected technical effects of millisecond-level low-latency persistence and high-throughput state archiving.

[0194] S7.4: Monitor the write completion status signal of the persistent memory snapshot chain archive file, and perform logical unloading and memory space release processing on the old snapshot nodes that have been successfully archived in the memory snapshot chain to generate a simplified memory snapshot chain instance that has been capacity optimized and retains only the nodes in the active window.

[0195] S7.5: Register cross-process shared handles and power-off recovery index pointers for persistent memory snapshot chain archive files, and establish a bidirectional association mapping between simplified memory snapshot chain instances and persistent memory snapshot chain archive files to form a final-state persistent memory snapshot chain archive system that supports high availability, disaster recovery, and elastic scaling.

[0196] Receive the simplified memory snapshot chain instance handle generated in step S7.4 and the persistent memory snapshot chain archive file path identifier created in step S7.3 as input objects for establishing a bidirectional association mapping.

[0197] The system calls the operating system kernel-level shared memory management interface to generate a read-only cross-process file descriptor based on the archive file path. This descriptor is then registered as a globally accessible shared handle, ensuring that the multi-inventory collaboration process has concurrent read permissions for historical state data.

[0198] Parse the version hash value of the starting node of the currently active window in the simplified memory snapshot chain instance, and set it as the target anchor point of the power failure recovery index pointer, which is directly mapped to the physical offset address of the corresponding data block in the persistent archive file.

[0199] Construct a structure object containing a shared handle reference count and a recovery index pointer, and use atomic operations to write the structure into the metadata area of ​​the memory snapshot chain control block to complete the logical binding between the thin instance and the persistent archive.

[0200] Register a file system event listener to monitor the integrity status of archived files. When an abnormal process termination signal is detected, trigger a fast loading mechanism based on the recovery index pointer to rebuild the memory snapshot chain baseline from the specified offset of the SSD.

[0201] Through the above-mentioned cross-process shared handle registration and power failure recovery index pointer binding process, the simplified memory snapshot chain of the previous step is transformed into a final state persistent memory snapshot chain archive system with high availability and disaster recovery capabilities and elastic scaling, achieving the expected technical effect of millisecond-level fault recovery and zero data loss.

[0202] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the steps of the method described in any of the above embodiments.

[0203] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0204] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this invention.

[0205] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains. The terms “first,” “second,” “third,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising” or “including” and similar terms mean that the elements or objects preceding “comprising” or “including” encompass the elements or objects listed following “comprising” or “including” and their equivalents, and do not exclude other elements or objects. The “multiple” mentioned in the embodiments of this application refers to two or more. A and / or B indicate three possibilities: A; B; and A and B.

[0206] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for collaborative management of unified inventory and real-time orders across multiple stores, characterized in that, Specifically, it includes: S1: For product SKUs in a multi-store unified inventory management scenario, construct a memory snapshot chain based on the real-time available inventory share, dynamic value weight coefficient, and the type of the most recent allocation trigger event of each store; S2: Generate an inventory rebalancing event based on the value assessment update signals in the new order data stream and store operation data stream generated by the order acquisition module; S3: Construct a dynamic collaboration graph between stores, and perform local feasibility determination and candidate allocation path enumeration on the inventory rebalancing event based on the dynamic collaboration graph between stores, so as to generate a local feasibility determination report. S4: Based on the local feasibility determination report, perform a prefix consistency check operation. If the check passes, generate an atomic new snapshot node. If the check fails, generate a temporary fulfillment instruction for the local store buffer pool. S5: Append the atomized new snapshot node to the end of the memory snapshot chain to complete the state transition, and broadcast the snapshot submission notification to the relevant store gateway based on the updated memory snapshot chain, thereby forming a closed loop of cross-store inventory redistribution; S6: For temporary fulfillment instructions in the local store buffer pool, enable the local store buffer pool of the corresponding store to perform temporary inventory deduction operations.

2. The multi-store unified inventory and real-time order collaborative management method according to claim 1, characterized in that, It also includes the following steps: S7: Dynamically manage the length of the memory snapshot chain, and persist old snapshot nodes that exceed the preset change number threshold to the local SSD storage medium through memory-mapped files.

3. The multi-store unified inventory and real-time order collaborative management method according to claim 1, characterized in that, The memory snapshot chain consists of lightweight, immutable snapshot nodes arranged in chronological order, where each snapshot node stores only the difference vector and version hash relative to the previous node to form the initial memory snapshot chain.

4. The multi-store unified inventory and real-time order collaborative management method according to claim 1, characterized in that, The inventory rebalancing event includes the target SKU identifier, the triggering source store ID, the current value deviation, and the timeliness constraint level.

5. The multi-store unified inventory and real-time order collaborative management method according to claim 1, characterized in that, The construction of the dynamic collaboration map between stores is specifically based on historical allocation success rate, logistics timeliness decay factor and real-time service load data.

6. The multi-store unified inventory and real-time order collaborative management method according to claim 1, characterized in that, The local feasibility assessment report includes the prefix consistency verification results.

7. The multi-store unified inventory and real-time order collaborative management method according to claim 5, characterized in that, S3 specifically includes: Acquire historical allocation success rate, logistics timeliness decay factor and real-time service load data, and perform multi-source heterogeneous data alignment and time window aggregation processing on the historical allocation success rate, logistics timeliness decay factor and real-time service load data to generate an initial collaborative relationship matrix. Based on the initial collaborative relationship matrix, a dynamic graph neural network is used to perform probability mapping on the historical transfer success rate between store nodes, exponential weighting calculation on the logistics timeliness decay factor, and normalization reverse correction on the real-time service load to generate a dynamic collaborative graph between stores. Receive pending inventory rebalancing events and generated inter-store dynamic collaboration graphs. Based on the graph traversal algorithm, search for connected subgraphs that meet the timeliness constraint level in the inter-store dynamic collaboration graphs with the triggering source store ID as the starting node, so as to generate an initial candidate path space containing a set of potential transfer target stores. For each potential allocation path in the initial candidate path space, a list of feasible candidate allocation paths is obtained by processing it. Based on the enumeration list of feasible candidate allocation paths, the logic of a single inventory share transfer is simulated and the virtual inventory status indicators after the transfer are pre-calculated. The simulation results are compared and analyzed with the prefix consistency verification rules to generate a local feasibility determination report containing the prefix consistency verification results and the priority of the recommended path.

8. The multi-store unified inventory and real-time order collaborative management method according to claim 7, characterized in that, The initial collaborative relationship matrix includes the basic attributes of store nodes and the original weights of edge connections.

9. The multi-store unified inventory and real-time order collaborative management method according to claim 7, characterized in that, The process of processing each potential allocation path in the initial candidate path space to obtain a list of feasible candidate allocation paths includes: For each potential allocation path in the initial candidate path space, a multi-dimensional constraint pruning operation is performed, combining the current value deviation information in the inventory rebalancing event, to generate a feasible candidate allocation path enumeration list.

10. The multi-store unified inventory and real-time order collaborative management method according to claim 4, characterized in that, S4 specifically includes: Obtain the difference vector between the candidate allocation path enumeration results in the local feasibility assessment report and the tail node of the current memory snapshot chain, and perform state transition simulation calculations on the candidate allocation path enumeration results to generate first-level intermediate state snapshot data containing the virtual inventory share and virtual value weight coefficient after the first state transition. Using the virtual inventory share and virtual value weight coefficient in the first-level intermediate snapshot data, combined with the timeliness constraint level of the inventory rebalancing event, a second-level state transition deduction process is performed to generate second-level intermediate snapshot data containing the final virtual inventory share and final virtual value weight coefficient after the second state transition. Based on the final virtual inventory share and final virtual value weight coefficient in the second-level intermediate snapshot data, a threshold comparison is made against the timeliness constraint level of the inventory rebalancing event. If the timeliness constraint level is met, an atomic submission flag is generated; otherwise, a temporary fulfillment instruction is generated in the local store buffer pool. For the generated atomic commit token, extract the difference vector between the version hash of the current memory snapshot chain tail node and the second-level intermediate snapshot data, and perform an immutable snapshot node encapsulation operation to generate an atomic new snapshot node.

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