Intelligent processing system and management method for cloud warehouse orders of omni-channel retail

Abstract

The application discloses a full-channel retail cloud warehouse order intelligent processing system and a management method, relates to the technical field of cloud warehouse intelligent scheduling, and first intercepts high-optimal orders, extracts a business constraint boundary, converts the business constraint boundary into a rigid business obligation set, and screens execution resources to generate a replaceable candidate performance combination set; then, available capacity upper limits are compared to mark overload resources, and same-name different-sense closed records are generated for orders that jointly occupy the overload resources; subsequently, for the closed records, a discrete particle swarm optimization algorithm is adopted, the rigid business obligation set is used to perform boundary constraint checking and filtering on scheme particles, and based on a ternary comparison fitness sequence, a final solution combination is evaluated and output; finally, a cloud warehouse resource state dictionary is updated, and an execution instruction is generated. The application decouples business rules and physical resources, effectively eliminates the resource squeeze and closed lock problem in a high-concurrency scenario, and improves the flexibility and robustness of scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent scheduling for cloud warehouses, and in particular to an intelligent processing system and management method for omni-channel retail cloud warehouse orders. Background Art

[0002] With the continuous evolution of retail business models, omni-channel operation has become the mainstream of the industry. Order requests from various platform channels, social channels and offline nodes have shown explosive growth. To meet service commitments in different dimensions, the business end usually assigns a preset high priority to key tasks. However, due to significant differences in the composition logic of order sources, the business attributes behind priorities present a highly heterogeneous state. During peak traffic periods, massive high-priority orders flood into the scheduling system in a very short time, which poses a huge challenge to the performance stability of cloud warehouses.

[0003] Traditional cloud warehouse scheduling solutions mainly rely on single-dimensional priority ranking or fixed rule allocation when processing demands from various channels. Existing data processing logic usually uniformly abstracts business demands of different natures into unadjustable strong constraint variables, and rigidly binds them to specific fulfillment channels or carriers directly. When multiple high-priority orders compete for limited local fulfillment resources together, such as inventory allowance at a specific node, warehouse operation efficiency or transfer capacity in the transportation link, since the scheduling logic regards a specific fulfillment path as an unchangeable constant, the calculation model will fall into a solution dilemma due to excessive overlapping of constraint boundaries. Due to the lack of in-depth analysis on the relationship between business obligations and execution carriers, the scheduling algorithm can hardly perform effective resource reconstruction in time when coping with resource congestion, which easily leads to optimization stagnation or data rollback at the global level, and further limits the overall flexibility of omni-channel resource allocation. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides an intelligent processing system and management method for omni-channel retail cloud warehouse orders, which solves the problem that in multi-channel high-concurrency scenarios, rigid binding of business attributes to execution carriers leads to local resource congestion and locking, which further causes optimization stagnation of the operation research model and limited scheduling flexibility.

[0005] To achieve the above objective, the present invention is implemented through the following technical solutions: An obligation unbinding module, configured to intercept order data elements meeting a preset high priority level, extract business constraint boundaries and convert them into a rigid business obligation set, screen objective execution resource entities from a global fulfillment resource set to generate a replaceable candidate fulfillment combination set, and perform fusion encapsulation to construct a standardized obligation unbinding result; The latch identification module is used to parse the standardized obligation unsealing results, compare the available capacity limit with the target resource demand to mark the overloaded resources, compare the order data elements that share the overloaded resources with the rigid business obligation set, and generate a latch record with the same name but different meaning when there are differences. The resource untangling module is used to iteratively update the candidate resource untangling scheme particles using the discrete particle swarm optimization algorithm for the same-named and different-meaning locking records. It performs boundary constraint verification and filtering on the candidate resource untangling scheme particles using a rigid business obligation set, performs scheme evaluation based on the ternary comparison fitness sequence of quantization overload and reconstruction cost, and outputs the final untangled fulfillment resource combination. The instruction generation module is used to compare the initial bound fulfillment resource combination with the final unbound fulfillment resource combination, generate the original resource release record and the new resource occupation allocation record, update the global cloud warehouse resource status dictionary using the concurrency control mechanism, and generate inventory locking task instructions and delivery dispatch reconstruction instructions that encapsulate a set of rigid business obligations.

[0006] Furthermore, the set of rigid business obligations includes multiple obligation elements defined using a triplet structure. These obligation elements include the type of obligation, the object of obligation protection, and the compliance conditions of the obligation. The target resource demand is converted into differentiated units based on the resource type of the objective resource entity.

[0007] Furthermore, the same-named different-semantic locking record includes a subset of high-priority orders participating in the same-named different-semantic locking record, a subset of resources jointly occupied and out of bounds by the same-named different-semantic locking record, and a subset of the obligatory unsealing results of the corresponding order subset; When there are overlapping order data elements or overloaded resources of the same capacity among the multiple identically named but different locking records generated, the locking identification module performs union and deduplication operations on the high-priority order subset, out-of-bounds resource subset, and obligation unsealing result subset contained in the multiple identically named but different locking records that intersect, and merges them to generate a global identically named but different locking record.

[0008] Furthermore, when the resource untangling module generates initial candidate resource untangling scheme particles, the first initial candidate resource untangling scheme particle inherits the initial bound fulfillment resource combination; The remaining initial candidate resource untangling scheme particles are generated by traversing the set of replaceable candidate fulfillment combinations and selecting candidate resource combinations that do not occupy capacity overload resources. Calculate the ternary comparison fitness sequence of the initial candidate resource untangling scheme particles, and initialize the historical individual optimal scheme and the global optimal scheme based on the ternary comparison fitness sequence.

[0009] Furthermore, the discrete particle swarm optimization algorithm includes a discrete crossover hybrid operator and a bounded boundary repair operator; The discrete cross-hybrid operator executes directional extraction logic based on the capacity overload resource occupancy status, detects whether the objective execution resource entity allocated to the order data element occupies the capacity overload resource, and when the capacity overload resource is detected, it prioritizes extracting the performance combination element from the historical individual optimal solution or the global optimal solution for coverage; When performing variable filling, the obligation boundary repair operator synchronously updates the virtual resource state view exclusive to the candidate resource unentanglement scheme particles. The virtual resource state view is physically isolated from the global cloud warehouse resource state dictionary.

[0010] Furthermore, the ternary comparison fitness sequence consists of the number of objective execution resources in the overloaded state, the normalization ratio of the overloaded resources, and the number of order data elements that have undergone resource changes; The ternary comparison fitness sequence adopts a lexicographical comparison rule from left to right, and sequentially performs the comparison of minimizing the number of objective execution resources in the overload state, the comparison of minimizing the normalized ratio of overloaded resources, and the comparison of minimizing the number of order data elements that have undergone resource changes.

[0011] Furthermore, the resource untangling module is configured with an abnormal blocking and suspension mechanism; When there is no valid alternative resource combination for an order data element, the resource untangling module maintains the initial binding fulfillment resource combination of the order data element and marks the order data element as reserved locked. When the discrete particle swarm optimization algorithm finishes its iteration and the number of objective execution resources in the overloaded state of the output global optimal solution is greater than zero, the resource untangling module truncates the operation allocation and throws a resource exhaustion exception.

[0012] Furthermore, the inventory locking task instruction and the delivery dispatch refactoring instruction couple and encapsulate the rigid business obligations into core fields; The instruction generation module generates inventory locking task instructions and delivery dispatch reconstruction instructions for order data elements that have not undergone resource changes or are marked as reserved and locked. However, it does not generate original resource release records and new resource allocation records for order data elements that have not undergone resource changes or are marked as reserved and locked.

[0013] Furthermore, the instruction generation module updates the global cloud warehouse resource status dictionary item by item using atomic operations or concurrent control lock mechanisms; During the update process, the instruction generation module verifies the consistency between the target resource requirement and the resource measurement unit of the objective execution resource entity, and prohibits numerical calculations across measurement units to ensure the global numerical consistency of system resource capacity data under multi-threaded concurrent scheduling.

[0014] Furthermore, a smart management method for omnichannel retail cloud warehouse orders is proposed and applied to the aforementioned smart order processing system for omnichannel retail cloud warehouses, including: Retain order data elements that meet the preset high priority level, extract business constraint boundaries and convert them into a set of rigid business obligations, filter objective execution resource entities from the global fulfillment resource set to generate a set of replaceable candidate fulfillment combinations, and integrate and encapsulate to construct standardized obligation unpacking results; The standardized obligation unpacking results are analyzed, and the available capacity limit is compared with the target resource demand to mark the overloaded resources. For the order data elements that share the overloaded resources, the rigid business obligation set is compared, and a same-named but different-meaning locking record is generated when there are differences. For locked records with the same name but different meanings, the discrete particle swarm optimization algorithm is used to iteratively update the candidate resource untangling scheme particles. The rigid business obligation set is used to perform boundary constraint verification and filtering on the candidate resource untangling scheme particles. The scheme evaluation is performed based on the ternary comparison fitness sequence of quantization overload and reconstruction cost, and the final untangled fulfillment resource combination is output. By comparing the initial bound fulfillment resource combination with the final unbound fulfillment resource combination, the original resource release record and the new resource occupation allocation record are generated. The global cloud warehouse resource status dictionary is updated using the concurrency control mechanism, and inventory locking task instructions and delivery dispatch reconstruction instructions encapsulating a set of rigid business obligations are generated.

[0015] Compared with existing technologies, it has the following advantages: This proposed omnichannel retail cloud warehouse order intelligent processing system and management method solves the problem of high-priority orders causing lock-in and resource constraints in a multi-channel environment by establishing a decoupling mechanism between business obligations and execution carriers. Traditional methods typically map business priorities to a single numerical value, causing scheduling models to rigidly lock resource binding relationships with business commitments when handling concurrent demands, making it difficult to perform dynamic reconfiguration without violating contracts. This solution maps commercial contracts to a standardized set of obligations and uses this to guide conflict identification logic, achieving accurate analysis of locked clusters of identically named but different meanings. Because the system extracts the boundaries of obligations that must be satisfied, it can identify replaceable elastic resources from a global perspective, thereby effectively releasing the solution space of operations research algorithms while preserving high-priority attributes. This fundamentally avoids the risk of model solution stagnation or scheduling oscillations caused by overly rigid constraints.

[0016] Building upon this foundation, this solution introduces an optimization strategy with boundary repair capabilities, further enhancing the robustness of the cloud warehouse scheduling system under resource-scarce conditions. By leveraging the logical isolation between the virtual state view and the global dictionary, computational safety is ensured during concurrent exploration of alternative paths, preventing interference with real-time data during the optimization process. By deeply encapsulating business obligations within the final generated task instructions, the constraint fidelity of scheduling results is ensured during cross-module flow, enabling the execution end to synchronously perceive and inherit the business bottom line from the decision-making end. This solution not only achieves flexible transformation of omnichannel resource scheduling, significantly reducing the probability of default on critical orders due to resource lockout, but also ensures the global consistency and operational accuracy of the cloud warehouse fulfillment system in complex business environments through automated suspension and degradation handling of extreme abnormal conditions. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the system framework of the present invention.

[0018] Figure 2 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figures 1 to 2 This application provides an intelligent order processing system for omnichannel retail cloud warehouses, including a mandatory unpacking module, a lockout identification module, a resource untangling module, and an instruction generation module; The mandatory unpacking module is used to decouple business attributes and objective attributes of orders with preset high priority levels, in order to construct a multi-dimensional constraint matrix that includes basic bottom-line rules and flexible resources. This module first synchronously receives the order demand set DS to be processed from various upstream data interfaces of the omnichannel retail system. The system then reads each order data element from the order demand set using a pre-built structured message parsing program. The system uses set Boolean matching conditions to perform a traversal comparison of the underlying attributes, strips out regular orders and directs them to the basic scheduling branch, and only retains orders that meet the high-priority trigger threshold to enter the deep processing link.

[0021] It should be noted that this high-priority trigger threshold is synchronized from the application programming interfaces of each sales channel on the front end, and is an objectively existing business parameter setting.

[0022] Specifically, the underlying attributes include order identification code, product code, product quantity, channel source, original channel priority, receiving area, and the initial binding fulfillment resource combination initially allocated by the initial warehousing system. This initial binding fulfillment resource combination includes the initial shipping warehouse, initial inventory batch, initial carrier, and initial delivery route. The system focuses on performing joint verification of channel source and original channel priority, intercepting non-core tasks, and establishing a standardized global digital image of orders for cloud warehouse scheduling, thereby freeing up memory overhead and bus bandwidth resources in high-concurrency scenarios.

[0023] For each order data element previously intercepted, the obligation unpacking module initiates a call request to the business obligation mapping table in the system kernel, extracts the business constraint boundaries with a preset high priority level for that order data element throughout its entire fulfillment lifecycle, and converts these business constraint boundaries into a set of rigid business obligations that can be processed by the computer. .

[0024] Specifically, the system uses the channel origin and product code of the order data element as the primary key of a composite index. Based on the structured order fields, it calls the system's built-in multi-condition rule engine to perform a Boolean matching query in the business obligation mapping table. The generated rigid business obligation set contains multiple obligation elements defined using a triplet structure. Each obligation element is represented by an obligation type indicating a categorical dimension. The object of the obligation to protect that is the focus of the action. And the compliance conditions of the obligations under the underlying Boolean equation. Together, they constitute a set of obligations. For example, for cold chain orders that include fresh produce, the system uses a rule engine to match and generate obligation elements where the obligation type is a goods handling obligation, the obligation protects the cold chain temperature control attribute, and the obligation compliance condition is that both the shipping warehouse and the carrier possess preset cold chain qualification marks. Through targeted extraction of structured fields and rule matching operations, multi-dimensional commercial contract terms are reduced in dimensionality and mapped into computer-readable system constraint variables.

[0025] It should be noted that this business obligation mapping table is configured as a dynamically hot-updable relational data table. Its table structure includes mapping rule number, applicable channel, triggering condition, obligation type, protected object, and compliance conditions. The system achieves incremental synchronization by monitoring the rule change logs of the front-end channel business systems. The dynamic mapping mechanism ensures that when sudden rule adjustments occur in various channels, the obligation unsealing module can output the latest compliance condition matrix in real time without refactoring the underlying logic code.

[0026] It should be noted that this set of rigid business obligations is a logical constraint matrix directly invoked by the operations research optimization model. In traditional mixed-integer linear programming models, high-optimal configuration usually means forcibly assigning extremely large penalty coefficients to all currently bound resource variables for overall freezing, thereby causing deadlocks of resources with the same name across multiple channels. This logical constraint matrix precisely segments the constant attributes that must be frozen and releases specific carrier identifiers or shipping warehouse codes as variable variables, fundamentally changing the topology of the operations research model and realizing the reconstruction of the underlying computational resource scheduling logic.

[0027] After establishing the business constraint boundaries, the obligation unpacking module retrieves the global fulfillment resource set (RS) from the cloud warehouse system. Based on product-level requirements, the system selects sufficient objective execution resource entities from the global fulfillment resource set. The system then combines the selected objective execution resource entities according to the performance link topology to generate preliminary actual performance path combinations. Subsequently, all preliminary actual performance path combinations are input into the rigid business obligation set for filtering and selection, ultimately forming a set of replaceable candidate performance combinations. .

[0028] Specifically, the system calculates the target resource demand based on the quantity of goods. Filter out available capacity An objective resource entity whose target resource requirement is greater than or equal to the target resource requirement. This target resource requirement is converted to different units based on the resource type. If the resource entity is inventory, the requirement equals the quantity of goods; if it is warehouse operational capacity, the requirement is determined by multiplying the quantity of goods by an operational conversion factor extracted from the system's pre-set product master data table; if it is a delivery route or carrier capacity, the total weight or volume of goods in the order is calculated and compared with the carrier's or route's current remaining load capacity threshold or available volume threshold. During the screening and comparison process, the system strictly verifies the resource measurement units of the target resource requirement and available capacity. Consistency is ensured, guaranteeing numerical verification with the same dimension and units. In the filtering phase, the system uses each obligation compliance condition to perform truth value judgments on the initial actual performance path combinations, recording only those combinations that pass truth value verification as valid candidate resource combinations. Meanwhile, the system inputs the initial binding of the order data element to the verification logic for secondary validation, and places it at the top of the set of replaceable candidate fulfillment combinations when the validation is compliant.

[0029] It should be noted that during the cross-validation of actual fulfillment path combinations and logical constraint matrices, there is a computational branch where resources are exhausted. When all actual fulfillment path combinations assembled from the global fulfillment resource set fail to satisfy the truth value verification of the rigid business obligation set, the generated set of replaceable candidate fulfillment combinations becomes empty. When this empty set state is detected, the system is configured with an exception blocking and suspension mechanism. The system throws a constraint breakdown exception, freezes the downward flow of the order data element, and marks the order data element as a resource-disrupted and suspended state. The system then triggers a manual review or business rule downgrade approval process, no longer pushing the order data element into the subsequent processing unit. This prevents fulfillment accidents caused by forced solutions from the logical level and bridges the computational gaps under extreme conditions.

[0030] After completing data dimensionality reduction and cleaning, the obligation unpacking module will represent the set of rigid business obligations that characterize the constraint boundaries. With the set of alternative candidate performance combinations representing the elastic solution space By performing integrated packaging, a standardized, mandatory unpacking result is jointly constructed. The unsealing result is then pushed across modules to the next level of the locking identification module.

[0031] Specifically, the results of the mandatory unpacking are expressed using the following formula: In the formula, This represents the standardized obligation unpacking result of the i-th order, and is used as the protocol data carrier for cross-module transmission; This represents the set of rigid business obligations corresponding to the i-th order, which maps the basic logical constraint boundary of the order in the scheduling refactoring. Let represent the set of alternative candidate fulfillment combinations corresponding to the i-th order, which includes all legal actual fulfillment path combinations that satisfy the above set of business obligations.

[0032] This unpacking of the obligation decouples and repackages business constraint data from execution resource path data. The system thereby establishes a unified data encapsulation protocol, breaking down the technical barriers of data heterogeneity between different sales channels, and transforming single-dimensional priority markers into multi-dimensional constraint matrices that combine constraint equations and flexible variable domains, providing highly consistent algorithm input data for the entire system.

[0033] The latch identification module is used to detect and identify conflicts and pressures on local resources caused by high-priority orders from multiple channels, based on the current occupancy status of the obligation unsealing data and objective execution resources, in order to generate a structured conflict cluster data carrier. The latch identification module receives standardized obligation unsealing results pushed across modules by the obligation unsealing module and simultaneously retrieves the current allocation status and available capacity limit of each objective execution resource entity in the cloud warehouse fulfillment resource set. .

[0034] Specifically, the locking identification module calculates the total demand of each objective execution resource entity under the current binding state based on the initial binding of the order data elements and the fulfillment resource combination. During the calculation process, the system will allocate the target resource requirements of high-priority orders within the same objective execution resource entity. The system performs cumulative calculations. It rigorously verifies the unit of measurement for the target resource demand and the upper limit of available capacity. To ensure consistency, the numerical values ​​are accumulated and compared under the same dimension and scale. If an objective execution resource entity meets the out-of-bounds condition that the total demand exceeds the upper limit of the available capacity, the latch identification module marks the objective execution resource entity as an overloaded resource and extracts all order data elements currently bound to the overloaded resource, aggregating them to form an initial subset of conflicting orders.

[0035] Specifically, for the generated initial subset of conflicting orders, the locking identification module performs name-for-name and name-for-name checks on business constraints. The system iterates through the set of rigid business obligations corresponding to each order data element within the subset of conflicting orders. If at least two order data elements exist within the initial conflicting order subset, and the types of obligations, objects of obligation protection, or compliance conditions contained in the rigid business obligation sets of these two order data elements are different, then the system determines that the conflicting order subset has triggered a semantic locking condition. The semantic verification mechanism is used to accurately identify complex conflict scenarios that appear to have a uniform high priority level but have drastically different underlying service contracts. If the rigid business obligation sets of all order data elements within the conflicting order subset are identical, it indicates that the conflict belongs to a homogeneous constraint squeeze. The locking identification module redirects it to the basic scheduling branch, where it is processed by a conventional homogeneous priority sorting algorithm (such as FIFO or amortized algorithm), and does not include it in the semantic untangling calculation scope of this module, thereby ensuring the specific boundary of the algorithm's processing objects.

[0036] Specifically, when a subset of conflicting orders simultaneously meets three conditions—all members have a preset high priority level, they share at least one overloaded resource, and there are discrepancies in business constraints—the locking identification module identifies this subset of conflicting orders as a high-priority subset participating in locking. Simultaneously, the system extracts the corresponding shared and overloaded objective execution resources, constructing a subset of out-of-bounds resources. The corresponding mandatory unpacking results are extracted and constructed into a subset of mandatory unpacking results. The locking identification module merges and encapsulates the above data objects to generate locking records with the same name but different meanings. .

[0037] The same-named, different-meaning locking record is expressed using the following formula: In the formula, Represents the u-th synonymous locking record, used as a structured data carrier to characterize conflict clusters; This represents a subset of high-priority orders participating in the locking record, mapping to the specific cluster of business orders that triggered a localized resource squeeze. This represents a subset of resources that are jointly occupied and out of bounds by the locking record, revealing the current execution bottleneck node of the cloud warehouse system; This represents the subset of obligations unpacking results for the corresponding order subset, which includes the set of rigid business obligations for each order and the set of alternative candidate fulfillment combinations, providing the basic solution space for subsequent untangling algorithms.

[0038] It should be noted that during the global concurrency detection process, if multiple identically named but different locking records contain overlapping order data elements or the same overloaded resources, the locking identification module will trigger a record merging mechanism. The system will perform union and deduplication operations on the high-priority order subset, out-of-bounds resource subset, and obligation unsealing result subset from the multiple identically named but different locking records that intersect, merging them into a single global identically named but different locking record. This merging mechanism eliminates the risk of the same order data element or the same objective execution resource entity being repeatedly called and cross-adjusted under multi-threaded concurrency, ensuring the uniqueness and global convergence of subsequent resource reconstruction algorithms.

[0039] It's important to note that the synonymous lockout records generated by the lockout identification module differ from the regular stockout alarm logs in traditional warehouse management systems. Regular stockout alarms trigger order blocking solely through single-dimensional quantity comparisons. This synonymous lockout record, however, takes a multi-dimensional constraint conflict perspective from operations research, transforming the phenomenon of resource squeeze into a multi-dimensional constraint matrix input source that includes order objects, out-of-bounds thresholds, and available solution space. By identifying the differences in business obligations, the lockout identification module avoids system-level failures such as indiscriminately freezing or forcibly rolling back all high-priority orders, providing targeted data anchors for subsequent flexible resource restructuring without violating commercial contracts.

[0040] The resource untangling module, while maintaining the business constraint boundaries, explores the elastic resource solution space to eliminate multi-dimensional resource squeeze conflicts in synonymous locking records. This module receives synonymous locking records from the locking identification module. The system parses locking records with the same name but different meanings and extracts a subset of high-priority orders participating in that locking record. A subset of resources that are jointly occupied and out of bounds by the locking record. and the corresponding order subset of the unpacking results subset For any order data element within the high-priority order subset, the system strictly reads the set of rigid business obligations and the set of replaceable candidate fulfillment combinations in the obligation unpacking result subset to ensure that the subsequent resource reconfiguration process does not exceed the preset rigid business constraint boundaries.

[0041] Specifically, the resource untangling module employs a discrete particle swarm optimization algorithm for combinatorial search. The system defines each candidate resource untangling scheme as a particle in the particle swarm. The candidate resource untangling scheme particle contains candidate fulfillment combinations that are reselected for each order data element within the high-priority order subset.

[0042] The candidate resource untangling scheme particle is expressed by the following formula: In the formula, Let v represent the v-th candidate resource untangling scheme particle, which maps to a solution space coordinate in the discrete search space; Representation of the scheme particle The resource combination selected for the i-th order data element is used to replace the initially bound fulfillment resource combination; k represents the total number of order data elements contained in the high-priority order subset, which is the upper limit of the particle dimension.

[0043] Specifically, before initiating iterative optimization, the system performs an initialization and construction operation of the discrete particle swarm. The system generates a particle swarm of a preset size based on the number of orders within the high-priority order subset. When generating initial candidate resource untangling scheme particles, the system employs a combination of directed heuristics and mutation: the first initial particle fully inherits the initial bound fulfillment resource combination of each order; the remaining initial particles are generated by traversing the set of replaceable candidate fulfillment combinations for each order, prioritizing combinations that do not consume capacity overload resources for differentiated cloning. The system calculates the ternary comparison fitness sequence of all initial particles and initializes the historical individual optimal scheme for each particle accordingly. And the globally optimal solution GB for the entire particle swarm.

[0044] Specifically, during the iterative optimization process, the system updates the positions of candidate resource untangling scheme particles based on discrete resource replacement rules. The resource untangling module's state update formula is as follows: In the formula, This represents the new scheme after particle update, and is used as the starting coordinate point for the next iteration; The candidate resource untangling scheme represents the historically optimal scheme in which the individual optimal fitness is achieved during the historical iteration process; GB represents the globally optimal scheme with the global optimal fitness in the current entire particle swarm. This represents a discrete cross-mixing operator used to inherit discrete performance combination elements from the current scheme particle, the historical individual optimal scheme, and the global optimal scheme; This indicates the obligation boundary repair operator, which is used to force backward correction of non-compliant solutions generated after cross-mixing.

[0045] Specifically, for discrete cross-mixing operators The system executes targeted extraction logic based on the overloaded resource occupancy state. For the i-th order data element in the candidate resource untangling scheme particles, the system detects whether this order data element is in the current scheme particle. Does the objectively allocated execution resource entity still occupy the out-of-bounds resource subset? If the system still uses overloaded resources, it will prioritize retrieving the best solution from historical data. Alternatively, the system can cover the fulfillment combination elements of the corresponding order data element in the globally optimal solution GB. If the resource shortage situation cannot be improved after covering, the system will selectively choose fulfillment combination elements that do not occupy overloaded resources from the set of replaceable candidate fulfillment combinations corresponding to the order data element, or select fulfillment combination elements in ascending order of overloaded resource occupancy to fill the variable. Through constraint-guided heuristic hybridization, the system avoids blindly searching the invalid solution space.

[0046] Specifically, regarding the obligation boundary repair operator The system performs boundary constraint verification and filtering. If a new resource combination allocated to an order data element disrupts the rigid business obligation set corresponding to the order data element, or does not belong to the replaceable candidate fulfillment combination set corresponding to the order data element, the system will directly eliminate the illegal resource combination. Subsequently, among the remaining legal candidate resource combinations, the system prioritizes resource combinations that can deterministically reduce the shared occupation of out-of-bounds resource subsets for variable filling, and synchronously updates the virtual resource state view dedicated to the particle of the candidate resource untangling scheme. This virtual resource state view is only used for the fitness calculation of the current particle and is physically isolated from the global cloud warehouse resource state dictionary to ensure state consistency and memory safety during multi-particle concurrent optimization. The system performs a global executability check on the modified new scheme, including business obligation invariance verification, resource combination legality verification, and global capacity over-limit prevention verification. After a preset number of iterations or after reaching the convergence condition, the resource untangling module outputs the globally optimal scheme as the final untangled fulfillment resource combination for each order data element. .

[0047] It should be noted that, in order to accurately quantify and compare the merits of different candidate resource untangling schemes and guide the discrete particle swarm out of local optima, a ternary comparison fitness sequence based on quantization overload and reconstruction cost was constructed. : In the formula, The ternary comparison fitness sequence of particles representing candidate resource untangling schemes is used as a comprehensive evaluation criterion for the algorithm. This represents the number of objective execution resources that are still in an overloaded state under the resource allocation scheme, and the unit of measurement is integers. It represents the normalized proportion of overloaded resources, mapping the relative depth of resource squeeze, and is a dimensionless parameter; This indicates the number of order data elements that have undergone resource changes in the scheme, and maps the operation cost of the system performing scheduling and refactoring.

[0048] The above normalized ratio The calculation formula is expressed as follows: In the formula, This indicates the particle in the candidate resource untangling scheme. Under the allocation status, for objective execution resource entities The target resource requirements are summarized. The calculation logic for this normalized ratio is as follows: For each objective execution resource entity in the out-of-bounds resource subset, the difference between its target resource requirement and the available capacity limit is calculated, and this difference is divided by the corresponding available capacity limit to obtain the individual overload ratio. Finally, the individual overload ratios of all objective execution resource entities are summed to obtain the global overload normalized ratio of this scheme. During the optimization comparison, the system follows a lexicographical comparison rule from left to right, successively pursuing the minimization of the number of overloaded resources, the minimization of the overload ratio, and the minimization of the number of resource change orders. This fitness sequence design ensures that the solution direction always converges towards the main objective of eliminating locking conflicts, and achieves precise control of reconstruction costs when multiple equivalent feasible solutions exist.

[0049] It should be noted that during the discrete resource replacement and obligation boundary repair processes, the system's underlying configuration includes anomaly blocking and suspension mechanisms. If, during the repair process of a candidate solution, the system detects that a certain order data element lacks any legal alternative resource combination, the system will maintain the initial bound fulfillment resource combination of that order data element and mark it as a reserved locked state in the internal state machine. If, at the end of the final iteration, the number of overloaded resources in the globally optimal solution is... If the value is still greater than zero, it indicates that the current synonymous locking record has no completely feasible solution without violating the commercial contract. At this point, the system truncates the allocation of resources for this locking cluster, throws a resource exhaustion exception, and redirects the associated orders to the manual review or delayed payment approval branch. This degradation mechanism prevents cascading out-of-bounds incidents caused by the algorithm forcibly solving solutions outside its scope, ensuring the logical closed loop of the system's computational flow and the robustness of the global algorithm.

[0050] The instruction generation module transforms the globally optimal solution output by the resource untangling module into an operational task that can be invoked by the underlying execution system. It also embeds basic business constraints into the issued task to complete the final delivery of cross-module fulfillment data. This module receives the final untangled fulfillment resource combination from the resource untangling module and the same-name, different-meaning lock records from the lock identification module. The system parses these data objects and extracts the initial bound fulfillment resource combination and the final untangled fulfillment resource combination for each order data element participating in the scheduling.

[0051] Specifically, for order data elements with a preset high priority level, the system performs a resource difference comparison operation. The system compares each objective execution resource entity in the initially bound fulfillment resource combination with the final unbound fulfillment resource combination. If differences exist between the two in terms of shipping warehouse, inventory batch, carrier, or delivery route, the system will generate an original resource release record for the changed objective execution resource entity. With new resource allocation records The system updates the global cloud warehouse resource status dictionary item by item based on the original resource release records and the new resource allocation records, using atomic operations or concurrency control lock mechanisms. During the status update process, the system strictly executes addition and deletion actions independently according to the resource number and enforces verification of the resource measurement unit of the operation value. Numerical calculations across units of measurement are prohibited to ensure global consistency of system resource capacity data under multi-threaded concurrent scheduling.

[0052] Specifically, based on the final untangled fulfillment resource combination, the system generates execution tasks with constraint identifiers for each order data element. The system also generates inventory locking task instructions for the inventory management process. And the refactoring instructions for delivery dispatching in the transportation management process. .

[0053] The inventory locking task instruction and the delivery dispatch reconfiguration instruction are expressed using the following formula: In the formula, This represents the inventory locking task instruction for the i-th order, and is a structured data message used to trigger the warehouse management system to execute the physical locking action; This represents a delivery dispatch reconfiguration instruction for the i-th order, used to trigger the transportation scheduling system to generate structured data messages for trunk or last-mile delivery tasks; The order identification code corresponding to the order data element is used as a unique index key for cross-system task flow; This indicates the product code contained in the order data element, which maps to the specific inventory category that needs to be transferred or locked; This indicates the number of products contained in the order data element, mapping the objective scale that needs to be locked or processed; and These represent the final shipping warehouse and the final inventory batch, respectively, guiding the operational nodes of the warehousing execution unit; and These represent the final carrier and the final delivery route, respectively, guiding the routing nodes of the transport execution unit; This represents the set of rigid business obligations corresponding to the order, which are the logical constraint boundaries that must still be strictly followed in subsequent warehousing and distribution operations.

[0054] Specifically, after generating individual instructions, the instruction generation module performs a global instruction aggregation operation. The system aggregates the inventory lock task instructions for all order data elements within the current processing batch into a global inventory lock instruction set (TKS), and aggregates all delivery dispatch reconstruction instructions into a global delivery dispatch reconstruction instruction set (TDS). Subsequently, the system pushes the global inventory lock instruction set across systems to the associated inventory management system and warehouse management system, and pushes the global delivery dispatch reconstruction instruction set to the associated transportation management system and delivery scheduling module, completing the implementation and transformation of the operational scheduling results.

[0055] It should be noted that for order data elements that did not undergo resource changes during the resource untangling phase, or were marked as locked due to resource depletion, the instruction generation module still generates corresponding inventory locking task instructions and delivery dispatch reconfiguration instructions, but does not generate original resource release records or new resource allocation records. This branching mechanism ensures that all orders with preset high priority levels within the processing batch can be sent to the execution end in a standardized data structure, avoiding gaps in the execution instruction flow and ensuring a complete closed loop of system data flow.

[0056] It should be noted that both the inventory locking task instruction and the delivery dispatch refactoring instruction constructed by the instruction generation module include a set of rigid business obligations. This core field has been encapsulated. Traditional warehousing and distribution operation instructions only contain static execution parameters such as goods and addresses. When the underlying system encounters sudden anomalies and needs to perform fault-tolerant reassignment, it is highly susceptible to service breaches due to the lack of upper-layer business constraints. This instruction structure deeply couples business rule data with underlying execution parameters, enabling the underlying system to synchronously inherit and perceive the rigid business constraint boundaries established by preceding modules when receiving scheduling tasks. This eliminates the risk of constraint loss during cross-module flow from the top level of the system architecture, achieving end-to-end constraint fidelity from business decapsulation to resource reconstruction to instruction issuance.

[0057] Furthermore, a smart management method for omnichannel retail cloud warehouse orders is proposed and applied to the aforementioned smart order processing system for omnichannel retail cloud warehouses, including: Retain order data elements that meet the preset high priority level, extract business constraint boundaries and convert them into a set of rigid business obligations, filter objective execution resource entities from the global fulfillment resource set to generate a set of replaceable candidate fulfillment combinations, and integrate and encapsulate to construct standardized obligation unpacking results; The standardized obligation unpacking results are analyzed, and the available capacity limit is compared with the target resource demand to mark the overloaded resources. For the order data elements that share the overloaded resources, the rigid business obligation set is compared, and a same-named but different-meaning locking record is generated when there are differences. For locked records with the same name but different meanings, the discrete particle swarm optimization algorithm is used to iteratively update the candidate resource untangling scheme particles. The rigid business obligation set is used to perform boundary constraint verification and filtering on the candidate resource untangling scheme particles. The scheme evaluation is performed based on the ternary comparison fitness sequence of quantization overload and reconstruction cost, and the final untangled fulfillment resource combination is output. By comparing the initial bound fulfillment resource combination with the final unbound fulfillment resource combination, the original resource release record and the new resource occupation allocation record are generated. The global cloud warehouse resource status dictionary is updated using the concurrency control mechanism, and inventory locking task instructions and delivery dispatch reconstruction instructions encapsulating a set of rigid business obligations are generated.

[0058] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An intelligent order processing system for omnichannel retail cloud warehouses, characterized in that: include: The obligation unsealing module is used to intercept order data elements that meet the preset high priority level, extract business constraint boundaries and convert them into a rigid business obligation set. The rigid business obligation set includes multiple obligation elements defined using a triple structure. Each obligation element includes an obligation type, an obligation protection object, and an obligation compliance condition. Objective execution resource entities are selected from the global performance resource set to generate a set of replaceable candidate performance combinations. The standardized obligation unsealing result is then fused and encapsulated to construct the standardized obligation unsealing result, which includes the rigid business obligation set and the set of replaceable candidate performance combinations. The latch identification module is used to parse the standardized obligation unsealing results, compare the available capacity limit with the target resource demand to mark overloaded resources, compare the rigid business obligation set for order data elements that jointly occupy overloaded resources, and generate a same-named different-meaning latch record when there are discrepancies. The discrepancy means that the obligation type, obligation protection object or obligation compliance conditions contained in the rigid business obligation set corresponding to at least two order data elements are different. The same-named different-meaning latch record includes a high-priority order subset participating in the same-named different-meaning latch record, a resource subset jointly occupied and out of bounds by the same-named different-meaning latch record, and a subset of obligation unsealing results of the corresponding order subset. The resource untangling module is used to iteratively update candidate resource untangling scheme particles using a discrete particle swarm optimization algorithm for locked records with the same name but different meanings. It uses a rigid business obligation set to perform boundary constraint verification and filtering on the candidate resource untangling scheme particles. It evaluates the execution scheme based on a ternary comparison fitness sequence that quantifies overload and reconstruction cost. The ternary comparison fitness sequence consists of the number of objective execution resources in the overload state, the normalization ratio of overload resources, and the number of order data elements with resource changes. The ternary comparison fitness sequence adopts a lexicographical comparison rule from left to right, and sequentially performs the minimum comparison of the number of objective execution resources in the overload state, the minimum comparison of the normalization ratio of overload resources, and the minimum comparison of the number of order data elements with resource changes, and outputs the final untangled fulfillment resource combination. The instruction generation module is used to compare the initial bound fulfillment resource combination with the final unbound fulfillment resource combination, generate the original resource release record and the new resource occupation allocation record, update the global cloud warehouse resource status dictionary using the concurrency control mechanism, and generate inventory locking task instructions and delivery dispatch reconstruction instructions that encapsulate a set of rigid business obligations.

2. The omnichannel retail cloud warehouse order intelligent processing system according to claim 1, characterized in that, The target resource demand is converted into differentiated units based on the resource type of the objective resource entity.

3. The omnichannel retail cloud warehouse order intelligent processing system according to claim 1, characterized in that, include: When there are overlapping order data elements or overloaded resources of the same capacity among the multiple identically named but different locking records generated, the locking identification module performs union and deduplication operations on the high-priority order subset, out-of-bounds resource subset, and obligation unsealing result subset contained in the multiple identically named but different locking records that intersect, and merges them to generate a global identically named but different locking record.

4. The omnichannel retail cloud warehouse order intelligent processing system according to claim 1, characterized in that, When the resource untangling module generates initial candidate resource untangling scheme particles, the first initial candidate resource untangling scheme particle inherits the initial bound fulfillment resource combination; The remaining initial candidate resource untangling scheme particles are generated by traversing the set of replaceable candidate fulfillment combinations and selecting candidate resource combinations that do not occupy capacity overload resources. Calculate the ternary comparison fitness sequence of the initial candidate resource untangling scheme particles, and initialize the historical individual optimal scheme and the global optimal scheme based on the ternary comparison fitness sequence.

5. The omnichannel retail cloud warehouse order intelligent processing system according to claim 4, characterized in that, Discrete particle swarm optimization algorithms include discrete crossover and hybrid operators and obligatory boundary repair operators; The discrete cross-hybrid operator executes directional extraction logic based on the capacity overload resource occupancy status, detects whether the objective execution resource entity allocated to the order data element occupies the capacity overload resource, and when the capacity overload resource is detected, it prioritizes extracting the performance combination element from the historical individual optimal solution or the global optimal solution for coverage; When performing variable filling, the obligation boundary repair operator synchronously updates the virtual resource state view exclusive to the candidate resource unentanglement scheme particles. The virtual resource state view is physically isolated from the global cloud warehouse resource state dictionary.

6. The omnichannel retail cloud warehouse order intelligent processing system according to claim 4, characterized in that, include: Configure the resource untangling module with an abnormal blocking and suspension mechanism; When there is no valid alternative resource combination for an order data element, the resource untangling module maintains the initial binding fulfillment resource combination of the order data element and marks the order data element as reserved locked. When the discrete particle swarm optimization algorithm finishes its iteration and the number of objective execution resources in the overloaded state of the output global optimal solution is greater than zero, the resource untangling module truncates the operation allocation and throws a resource exhaustion exception.

7. The omnichannel retail cloud warehouse order intelligent processing system according to claim 6, characterized in that, include: The inventory locking task instruction and the delivery dispatch refactoring instruction encapsulate the rigid business obligations into core fields. The instruction generation module generates inventory locking task instructions and delivery dispatch reconstruction instructions for order data elements that have not undergone resource changes or are marked as reserved and locked. However, it does not generate original resource release records and new resource allocation records for order data elements that have not undergone resource changes or are marked as reserved and locked.

8. The omnichannel retail cloud warehouse order intelligent processing system according to claim 1, characterized in that, include: The instruction generation module updates the global cloud warehouse resource status dictionary item by item using atomic operations or concurrency control lock mechanisms; During the update process, the instruction generation module verifies the consistency between the target resource requirement and the resource measurement unit of the objective execution resource entity, and prohibits numerical calculations across measurement units to ensure the global numerical consistency of system resource capacity data under multi-threaded concurrent scheduling.

9. A method for intelligent management of omnichannel retail cloud warehouse orders, applied to the omnichannel retail cloud warehouse order intelligent processing system described in any one of claims 1-8, characterized in that, Includes the following steps: Order data elements that meet the preset high priority level are intercepted, business constraint boundaries are extracted and converted into a rigid business obligation set. The rigid business obligation set includes multiple obligation elements defined using a triplet structure. Each obligation element includes an obligation type, an obligation protection object, and an obligation compliance condition. Objective execution resource entities are screened from the global performance resource set to generate a set of replaceable candidate performance combinations. The standardized obligation decapsulation result is then fused and encapsulated to construct the standardized obligation decapsulation result, which includes the rigid business obligation set and the set of replaceable candidate performance combinations. The standardized obligation unsealing results are analyzed, and the available capacity limit is compared with the target resource demand to mark overloaded resources. For order data elements that share overloaded resources, the rigid business obligation set is compared. When there are discrepancies, a same-named discrepancy locking record is generated. The discrepancy means that the obligation types, obligation protection objects, or obligation compliance conditions contained in the rigid business obligation sets corresponding to at least two order data elements are different. The same-named discrepancy locking record includes a high-priority order subset participating in the same-named discrepancy locking record, a resource subset that is shared by the same-named discrepancy locking record and exceeds the limit, and a subset of obligation unsealing results of the corresponding order subset. For locked records with the same name but different meanings, a discrete particle swarm optimization algorithm is used to iteratively update the candidate resource untangling scheme particles. Boundary constraint verification and filtering are performed on the candidate resource untangling scheme particles using a rigid business obligation set. The execution scheme is evaluated based on a ternary comparison fitness sequence that quantifies overload and reconstruction cost. The ternary comparison fitness sequence consists of the number of objective execution resources in the overload state, the normalization ratio of overload resources, and the number of order data elements with resource changes. The ternary comparison fitness sequence adopts a lexicographical comparison rule from left to right, and sequentially performs the minimum comparison of the number of objective execution resources in the overload state, the minimum comparison of the normalization ratio of overload resources, and the minimum comparison of the number of order data elements with resource changes, and outputs the final untangled fulfillment resource combination. By comparing the initial bound fulfillment resource combination with the final unbound fulfillment resource combination, the original resource release record and the new resource occupation allocation record are generated. The global cloud warehouse resource status dictionary is updated using the concurrency control mechanism, and inventory locking task instructions and delivery dispatch reconstruction instructions encapsulating a set of rigid business obligations are generated.

Images