A cloud warehouse delivery order allocation method

Abstract

The application relates to the technical field of warehouse logistics scheduling, and discloses a cloud warehouse delivery order allocation method, which comprises the following steps: receiving a delivery order, performing multidimensional compliance verification, calculating a complexity dimension value, generating an initial delivery wave pool, constructing a full-dimension inventory image, establishing a space mapping association between available inventory and terminal execution equipment, collecting equipment microscopic physical characteristic data, substituting into a fault prediction model to calculate a fault occurrence probability, determining that the equipment is in a sub-health state if the probability is greater than a warning threshold, executing wave unbinding logic to route high-complexity orders to healthy equipment, executing dynamic dimension reduction allocation logic to assign low-complexity orders to the sub-health equipment, generating a global delivery allocation scheme, and issuing a delivery execution instruction after passing the feasibility verification. The application avoids the problem that a single-point hardware sudden failure causes a complex wave task to be stalled for a long time, and improves the overall stability when multiple devices cooperate to deliver.

Description

Technical Field

[0001] This invention relates to the field of warehousing and logistics scheduling technology, specifically a method for allocating outbound orders from a cloud warehouse. Background Technology

[0002] With the development of automated warehousing technology, cloud warehouse systems typically employ automated terminal equipment such as stacker cranes and shuttle vehicles for outbound operations. Existing outbound order allocation systems primarily rely on preset business rules and the current online / offline status of the equipment for task assignment. However, in real-world scenarios with continuous high concurrency, this conventional allocation method has certain limitations.

[0003] Existing scheduling systems cannot identify in advance the sub-optimal health of equipment due to wear and tear of mechanical parts or aging of electrical components when assigning orders. When processing complex wave orders containing multiple independent goods and requiring merging across different aisles, a sudden downtime of the assigned equipment can cause other goods in the associated wave to wait indefinitely in the downstream sorting area due to the single point of failure disrupting the picking process. This sudden downtime can trigger wave task deadlock, reducing the overall efficiency of multi-equipment collaborative outbound operations.

[0004] Meanwhile, for equipment with operational risks, existing systems typically handle them by directly issuing shutdown orders and generating repair work orders, or ignoring the potential risks and continuing to assign routine consolidation outbound tasks. Direct shutdown for repair results in the loss of physical handling capacity in that aisle area, leading to excessive idle equipment resources; while continuing to assign routine tasks will cause widespread order processing delays should the equipment experience a real failure. Current technology lacks a mechanism to adjust the order allocation structure based on the degree of equipment health risk, failing to achieve a balance between controlling the business impact of equipment failures and maintaining the system's basic capacity.

[0005] Furthermore, even if some warehousing systems attempt to introduce fault diagnosis rules or models, existing prediction mechanisms mostly rely on static parameter configurations. As the service life of warehousing equipment extends, natural wear and tear on the mechanical structure causes long-term shifts in the underlying microscopic physical characteristics of the equipment. Due to the lack of a model error correction mechanism based on real historical operational results, static models cannot adaptively adjust parameters according to actual operating logs, resulting in a gradual decrease in the system's accuracy in predicting the sub-health state of equipment over time. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a cloud warehouse outbound order allocation method, which solves the problem that existing warehouse equipment scheduling systems only allocate tasks based on the binary online status of the equipment, and cannot cope with sudden downtime, wave task deadlock, and related sorting line obstruction caused by sub-healthy equipment operation.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a cloud warehouse outbound order allocation method, comprising the following steps: The core allocation layer receives outbound orders from the upstream business layer and performs multi-dimensional compliance verification, quantifies the complexity dimension value, and generates the initial outbound wave pool. The core allocation layer constructs a full-dimensional inventory profile including physical location coordinates, and establishes a spatial mapping relationship between available inventory and terminal execution devices based on the physical location coordinates. The device linkage layer collects microscopic physical characteristic data of the terminal execution devices; The core allocation layer substitutes it into the equipment failure prediction model to calculate the probability of failure and makes a judgment: If the probability of a fault occurs is greater than the warning threshold, the terminal execution device is determined to be in a sub-healthy state. Based on spatial mapping association, the predictive wave unbinding logic is executed, and the orders originally planned to be allocated in the initial outbound wave pool and whose complexity dimension value is greater than or equal to the order unbinding judgment threshold are routed to healthy devices. The dynamic dimensionality reduction allocation logic is executed to assign low-complexity orders less than the order complexity dimensionality reduction threshold to the terminal execution device, and a global outbound allocation scheme is generated. The equipment linkage layer performs a feasibility check on the global outbound allocation plan. Once the check is passed, the core allocation layer freezes the corresponding inventory and issues an outbound operation execution instruction.

[0008] Preferably, the core allocation layer performs multi-dimensional compliance verification, which specifically includes: verifying ownership of goods by comparing the owner's identity in the outbound order with the ownership field in the inventory database; calculating the total available inventory after deducting the allocated locked inventory, the inventory frozen for inventory count, and the loss frozen inventory from the total book inventory, and performing inventory availability verification; and performing contract rule compliance verification by comparing the batch number or production date specified in the order.

[0009] Preferably, the core allocation layer quantitative calculation of the complexity dimension value specifically includes: extracting the number of independent SKU types, the total number of goods, and the basic priority weight coefficient contained in each order, combining them with the preset normalized weight coefficient, and weightedly calculating the discrete picking complexity component, the handling scale complexity component, and the fulfillment timeliness scheduling complexity component, and summing each component to obtain the complexity dimension value of the corresponding order.

[0010] Preferably, the core allocation layer establishes a spatial mapping association between available inventory and terminal execution equipment based on the physical location coordinate field. Specifically, this includes: extracting the warehouse area number, aisle number, row number, layer number, and column number of the physical location of the available inventory; performing range matching in the pre-set equipment topology relationship table within the system; locating the target terminal execution equipment responsible for the three-dimensional boundary range of the physical space; and transforming the available inventory in the business dimension into a candidate pool of equipment tasks in the physical dimension.

[0011] Preferably, the microscopic physical characteristic data collected by the equipment linkage layer specifically includes: the absolute value of motor current fluctuations obtained by sensors, the calibration reference current, the actual acceleration during the start-up acceleration and deceleration phase, the calibration reference acceleration issued by motion control commands, and the frequency of abnormal vibrations during high-speed operation.

[0012] Preferably, the equipment failure prediction model is constructed based on the Logistic regression algorithm. The core allocation layer calculates the relative frequency of abnormal motor current fluctuations based on the absolute value of motor current fluctuations and the calibration reference current, and calculates the degree of mechanical transmission efficiency attenuation based on the actual acceleration and the calibration reference acceleration. The above two items, together with the abnormal vibration frequency, are used as continuous state parameters. Combined with the feature weight coefficient and the system basic environment offset, the probability of failure of the output terminal execution equipment in the next wave cycle is calculated.

[0013] Preferably, the core allocation layer executes the predictive wave unbinding logic specifically including: extracting the complexity dimension value of the wave orders allocated to the terminal execution device in the sub-healthy state; when the complexity dimension value is greater than or equal to the order unbinding judgment threshold, the core allocation layer forcibly strips the corresponding wave orders and searches for homogeneous redundant available inventory in the lanes managed by healthy devices in the normal available state for rematching and binding.

[0014] Preferably, the core allocation layer executes dynamic dimensionality reduction allocation logic specifically including: marking outbound orders in the global order pool with a complexity dimension value less than the order complexity dimensionality reduction threshold as low-complexity orders, extracting them to construct an independent dimensionality reduction allocation candidate queue, and performing a targeted available inventory matching query within the physical lane area managed by the terminal execution device in a sub-healthy state, and forcibly assigning the successfully matched low-complexity orders to the corresponding terminal execution device for execution.

[0015] Preferably, the feasibility verification of the global outbound allocation scheme by the equipment linkage layer specifically includes: counting the number of outstanding tasks currently being executed by each device and accumulating the number of tasks to be executed assigned to the corresponding devices in the newly issued allocation scheme, and determining whether the sum of the task counts is greater than the maximum operating capacity to verify the operation queue capacity; extracting the starting physical location coordinates and target outbound port coordinates of the relevant devices and calculating the three-dimensional motion trajectory curve, and determining the spatial overlap to verify the anti-collision of the physical operation path; for tasks that fail the verification, the core allocation layer unlocks them and returns them to the waiting allocation pool for re-optimization and matching of inventory.

[0016] Preferably, after issuing the warehouse operation execution instruction, an adaptive reverse calibration step is also included: the core allocation layer relies on the actual operation logs collected by the equipment linkage layer to extract the downtime abnormal records of the terminal execution equipment within a set time window as positive samples and normal operation records as negative samples, and together with the collected microscopic physical feature data, to form a training sample sequence. The overall prediction error rate is calculated using the cross-entropy loss function, and the feature weight factors inside the equipment fault prediction model are iteratively updated through the gradient descent algorithm.

[0017] This invention provides a method for allocating outbound orders from a cloud warehouse. It has the following beneficial effects: 1. This invention collects microscopic physical characteristic data of terminal execution devices and calculates the probability of failure by combining it with a fault prediction model. It can identify sub-healthy states before physical downtime of the devices. For sub-healthy devices, the system executes predictive wave unbinding logic to forcibly strip the originally planned high-complexity orders and reroute them to healthy devices. This avoids the problem of long-term stagnation of complex wave tasks caused by sudden hardware failures at a single point and improves the overall stability of multi-device collaborative outbound operations.

[0018] 2. This invention assigns low-complexity orders to terminal execution devices in a sub-healthy state by executing dynamic dimensionality reduction allocation logic. Since low-complexity orders have a simple structure and do not involve cross-device task merging, assigning them to devices with potential downtime risks not only utilizes the remaining operating cycle of the device and maintains the basic handling capacity of the warehouse, but also limits the business impact caused by the failure of the device to the scope of a single order, reducing the difficulty of subsequent manual anomaly handling.

[0019] 3. This invention introduces an adaptive reverse calibration mechanism. Positive and negative samples are constructed by extracting the actual operating logs of the terminal execution equipment and forming a training sample sequence by combining microscopic physical feature data. The system uses the cross-entropy loss function to calculate the prediction error rate and then iteratively updates the feature weight factors inside the fault prediction model. It can automatically adjust parameters according to the actual mechanical wear of the warehousing equipment and continuously improve the accuracy of the sub-health status judgment of the equipment. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the system functional architecture according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the method flow according to an embodiment of the present invention; Figure 3 This is a schematic diagram comparing the device operating status and failure rate according to an embodiment of the present invention. Figure 4 This is a schematic diagram comparing system throughput and deadlock frequency in an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions in 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.

[0022] Please see the appendix Figure 1 The present invention provides a cloud warehouse outbound order allocation system, which may include: an upstream business layer, a core allocation layer, a device linkage layer and a terminal execution layer.

[0023] The upstream business layer is responsible for connecting with external enterprise resource planning systems and external e-commerce platforms. The upstream business layer receives outbound orders, cargo owner contract rules and basic product information from external sources, and provides data input interfaces.

[0024] The core allocation layer is responsible for performing outbound order preprocessing, full-dimensional inventory matching, equipment sub-health status prediction calculation, wave order unbinding, and dynamic reallocation calculation. The core allocation layer is equipped with a rule engine to receive and store allocation rules for various product categories and cargo owners.

[0025] The equipment linkage layer is responsible for providing real-time online status and job queue load data for the equipment. Simultaneously, the equipment linkage layer frequently collects microscopic physical characteristic data from the underlying equipment, reports this data to the core allocation layer, and receives outbound execution instructions from the core allocation layer.

[0026] The terminal execution layer includes stacker cranes, shuttle cars, and on-site sorting equipment in the automated storage and retrieval system (AS / RS). The terminal execution layer performs specific outbound logistics actions and utilizes sensors configured at the lower levels to collect real-time physical characteristic data of the machinery during operation.

[0027] The cloud warehouse outbound order allocation system establishes a real-time data synchronization mechanism between its various layers. The core allocation layer interacts with the enterprise resource planning system through an interface to synchronize product details and outbound order data. The core allocation layer also synchronizes data with the equipment linkage layer via an Ethernet interface. The equipment linkage layer provides real-time feedback to the core allocation layer on instruction execution progress, storage location availability, and equipment micro-physical characteristics. All data synchronization processes are encrypted, and the core allocation layer records operation logs for subsequent data backtracking and querying.

[0028] See attached document Figure 2 This invention provides a method for allocating outbound orders from a cloud warehouse, which may include the following steps: The system receives outbound orders from the upstream business layer, performs standardized parameter parsing on these orders, and executes multi-dimensional compliance checks such as ownership verification, total inventory verification, and batch expiration date verification. Orders that pass the checks are prioritized based on their basic attributes, and the complexity dimension value of each outbound order is quantitatively calculated. Orders with the same attributes are merged to generate an initial outbound wave pool.

[0029] The core allocation layer is used to build a full-dimensional inventory profile for inventory products, screen available inventory pools that meet compliance requirements, and establish a mapping relationship between inventory locations and underlying terminal execution devices. At the same time, the micro-physical characteristic data of each device reported by the device linkage layer is obtained, and the prediction model in the core allocation layer is used to calculate the probability of failure of each device within a preset period.

[0030] The calculated failure probability is compared with the preset warning threshold. When the failure probability is less than or equal to the warning threshold, the corresponding equipment is determined to be in a healthy state. For the candidate inventory in the corresponding lane of the healthy equipment, the wave order and available inventory are matched and bound based on the multi-objective optimization algorithm.

[0031] When the probability of a fault exceeds the warning threshold, the corresponding equipment is determined to be in a sub-healthy state. For sub-healthy equipment, a predictive wave unbinding logic is executed. The system proactively removes high-complexity outbound wave orders originally planned for the corresponding lane of the sub-healthy equipment and reroutes them to the lane where healthy equipment is located. Subsequently, a dynamic dimensionality reduction allocation logic is executed. The system selects orders below the complexity threshold from the allocation pool and matches these low-complexity orders to the sub-healthy equipment for execution.

[0032] All generated allocation schemes undergo a secondary compliance verification. The verified allocation schemes are then synchronized to the equipment linkage layer for feasibility verification of the job queue capacity and physical operating paths. Upon successful feasibility verification, the core allocation layer freezes the corresponding matching inventory and issues outbound task instructions to the equipment linkage layer for execution.

[0033] During the outbound operation, the system tracks the picking status of each storage location and equipment operation data in real time. When an equipment downtime or inventory discrepancy is detected, the system unlocks the inventory of the abnormal storage location and re-executes inventory optimization matching and instruction issuance for the affected incomplete orders.

[0034] Once all goods have been picked up and inspected, the core allocation layer confirms the completion of the outbound task and deducts the corresponding inventory. The system synchronizes the outbound results and inventory change information to the enterprise resource planning system to complete the accounting loop, while simultaneously collecting equipment operation data during the actual execution process to update the feature weight parameters within the prediction model.

[0035] Before executing the core allocation logic, the core allocation layer performs standardized parsing, multi-dimensional compliance verification, and complex metric evaluation on the received outbound pre-orders.

[0036] The core allocation layer receives outbound pre-orders from the upstream business layer through a standardized data interface. The core allocation layer extracts key parameters from these pre-orders, including the shipper's identity, unique order number, SKU details, required quantity, batch requirements, expiry date requirements, fulfillment deadline, delivery address information, outbound priority weight, and platform reservation information. For received non-standardized order formats, the core allocation layer uses a pre-defined parsing template to convert them into structured data objects that are uniformly recognized by the system.

[0037] After extracting the parameters, the core allocation layer executes a multi-dimensional compliance verification procedure. This multi-dimensional compliance verification specifically includes: Ownership verification: The core allocation layer retrieves the owner's identity identifier from the outbound order and compares it with the ownership field in the inventory database to confirm that the requested SKU physically belongs to the owner. If the requested SKU exceeds the owner's legal ownership scope, the system records it as an ownership anomaly and blocks the order.

[0038] Inventory Availability Verification: The core allocation layer retrieves the real-time available inventory total for the corresponding SKU under the corresponding owner. The available inventory total is calculated by deducting allocated locked inventory, inventory frozen pending inventory count, and inventory frozen due to losses from the total book inventory. The core allocation layer then determines whether the available inventory total is greater than or equal to the order demand quantity.

[0039] Contract rule compliance verification: The core allocation layer calls the preset cargo owner contract rule library to compare whether the batch number or production date specified in the order conforms to the outbound rules agreed in the contract. For cargo owners whose contracts stipulate that the near-expiry date must be shipped first, the system verifies whether the SKU in the order request contains batches that have expired or are in a restricted sales period.

[0040] Data format integrity verification: The core allocation layer determines whether there are missing items in the order parameters, such as incomplete delivery address or incorrect SKU encoding format.

[0041] For orders that pass the multi-dimensional compliance checks mentioned above, the core allocation layer stores them in the allocation pool and performs order priority ranking. The priority ranking is calculated by the core allocation layer according to a preset weight table. Urgent orders with remaining fulfillment deadlines less than a preset threshold are assigned the highest priority; fresh produce or near-expiry goods orders are next; followed by orders from major promotional events, regular sales orders, transfer orders, and orders for the transfer of ownership. Within orders of the same attribute, the core allocation layer sorts them in ascending order according to their fulfillment deadlines.

[0042] After prioritizing the orders, the core allocation layer performs a complexity quantification calculation on each order to be allocated. The system introduces an order complexity dimension value to characterize the order's occupancy of warehousing and logistics resources and the difficulty of the sorting operation. The formula for calculating the order complexity dimension value is as follows: ; In the formula: This represents the complexity dimension value of the outbound order; This indicates the number of individual SKU types included in the outbound order, reflecting the degree of diversity in physical space where the order is picked up across aisles or storage locations; This indicates the total number of items included in the outbound order; This represents the basic priority weighting coefficient for the outbound order; , , These represent the normalized weight coefficients of the corresponding feature terms; This represents the discrete picking complexity component resulting from the diversity of SKU types; This represents the handling scale complexity component resulting from the total amount of goods leaving the warehouse; This represents the scheduling complexity component of fulfillment timeliness generated by the order priority weight.

[0043] The core allocation layer categorizes and labels orders based on the calculated complexity dimension value. Further, the core allocation layer performs a wave merging operation. It filters out orders with the same shipper identity, the same outbound type, and the same platform reservation information. Under the premise of meeting the preset wave capacity threshold, the system merges multiple single orders into an outbound wave, generating an outbound wave task. Regarding the complexity dimension value... For orders exceeding the preset limit, the core allocation layer executes order splitting logic, dividing them into multiple sub-tasks.

[0044] The order data described above, after standardized parsing, compliance verification, prioritization, and complex quantification, forms the foundational input for subsequent intelligent inventory matching and collaborative allocation of equipment in sub-optimal condition. For orders rejected during the verification process, the core allocation layer generates anomaly feedback information and synchronizes it to the upstream business layer, recording the reasons for the anomalies for handling by suppliers or operations personnel.

[0045] After completing the multi-dimensional compliance verification and complex quantification assessment of outbound orders, the core allocation layer performs the construction of a full-dimensional available inventory pool and the physical mapping of underlying terminal execution devices.

[0046] The core allocation layer constructs a comprehensive inventory profile for each smallest unit of inventory stored in the warehouse, based on real-time accounting data and physical location data from the warehouse management system. This comprehensive inventory profile includes several fundamental data features, specifically owner identifier, product code, batch attribute, expiration date, physical location coordinates, and inventory status markers. The inventory status markers distinguish the current business stage of the inventory entity, categorized as available, locked by order, frozen for inventory checks, and frozen for quality inspection.

[0047] For outbound orders that have passed compliance verification, the core allocation layer performs targeted searches within the full-dimensional inventory profile based on the demand parameters parsed from the order, initially filtering out a set of candidate inventory that meets the criteria. During the search and filtering process, the system executes status filtering logic, removing inventory in locked, inventory frozen, or quality inspection frozen states, and aggregating the remaining inventory as available to generate the available inventory pool for each order. For concurrent deductions and data status updates of regular book inventory, those skilled in the art can use database row-level locking or distributed transaction control mechanisms for processing; the processing and implementation methods are well-known technologies in the field and will not be elaborated here.

[0048] After generating the available inventory pool, the core allocation layer establishes a spatial mapping relationship between available inventory and terminal execution equipment based on the physical location coordinate field in the full-dimensional inventory profile. The physical location coordinate field includes the storage area number, aisle number, row number, floor number, and column number of the inventory entity in the automated storage and retrieval system (AS / RS). The core allocation layer queries the pre-configured equipment topology table within the system, which records the three-dimensional boundary range of the physical space managed by each terminal execution equipment in the AS / RS. Terminal execution equipment includes stacker cranes responsible for vertical and horizontal handling, and four-way shuttles responsible for operations in dense storage areas, among other logistics hardware entities.

[0049] The core allocation layer extracts the corresponding parameters from the physical location coordinate field, performs range matching in the equipment topology table, and locates the corresponding terminal execution equipment responsible for operations in that physical area. The quantitative expression of equipment mapping relationships is achieved through the following spatial association mapping function: ; In the formula: This represents a unique identifier for the target terminal execution device that has been matched. This represents the set of physical storage location coordinates extracted from the full-dimensional inventory profile; This represents a spatial association mapping function established based on a device topology table.

[0050] By executing the aforementioned mapping function, the system completely transforms the available inventory pool from the business dimension into a candidate pool of device tasks from the physical dimension. Each physical location containing available inventory is uniquely bound to a specific terminal execution device. After binding, these terminal execution devices are included in the dynamic monitoring scope of the core allocation layer and used as the target objects for subsequent high-frequency collection of micro-physical characteristic data and execution of sub-health state prediction models. This mapping process eliminates the information silos between upstream order logic and downstream hardware execution, providing a basic data structure support for cross-domain collaborative allocation.

[0051] After completing the construction of the full-dimensional available inventory pool and device mapping, the device linkage layer performs high-frequency collection of the underlying micro-physical characteristics of the target terminals within the monitoring range.

[0052] The target terminal execution equipment has various underlying sensors deployed on its mechanical transmission structure and electrical control circuit. Specific lower-level hardware implementations include a high-precision current transformer installed in the main drive motor power supply circuit, a triaxial accelerometer fixed to the mechanical support chassis, and a piezoelectric vibration sensor attached near the contact surface between the gear reducer and the guide rail. The equipment linkage layer establishes industrial fieldbus communication with these sensors through the underlying programmable logic controller, reading raw physical parameters during equipment operation at millisecond-level sampling periods.

[0053] The equipment linkage layer extracts the real-time operating current of the main drive motor during the handling operation. The system extracts the actual current sampling sequence during the stable operation phase of the motor within the current work cycle and calculates its effective value. The equipment linkage layer stores the calibration reference current of various motor models under no-load and standard load conditions. The system calculates the difference between the actual current effective value and the calibration reference current to obtain the absolute value of the motor current fluctuation. This absolute value of fluctuation directly represents the additional power consumption generated by the motor when overcoming abnormal friction or internal resistance of the track, and is used to assess the health status of the electrical drive system.

[0054] The equipment linkage layer acquires the dynamic physical quantities of the target terminal execution equipment during the acceleration and deceleration phases at the start of operation. A three-axis accelerometer records the motion parameters of the equipment in real time from a standstill to its rated operating speed, and the equipment linkage layer extracts the actual acceleration data during this phase. The system synchronously reads the calibration reference acceleration issued by the current motion control command from the internal registers of the programmable logic controller. Under conditions of mechanical wear, aging transmission belts, or poor bearing lubrication, the actual response capability of the equipment will decrease. The degree of deviation between the actual acceleration and the calibration reference acceleration is captured by the system and serves as a core indicator for measuring the decline in mechanical transmission efficiency.

[0055] The equipment linkage layer collects vibration data of the mechanical transmission mechanism during high-speed operation. Piezoelectric vibration sensors continuously convert the mechanical vibration of the mechanical structure into a sequence of electrical signal waveforms. The system's preprocessing module in the equipment linkage layer sets a safety threshold for the amplitude corresponding to the equipment model. Within a set unit time window, the system compares the vibration waveform to the threshold and counts the number of discrete pulses whose amplitude exceeds the safety threshold. The system defines this count as the abnormal vibration frequency per unit time, which characterizes the degree of deterioration of surface defects on the guide rail or abnormal gear meshing. For low-pass filtering, fast Fourier transform, and other frequency domain conversions and basic feature extraction of the original analog signal from the sensor, those skilled in the art can use conventional digital signal processing algorithms. The analog-to-digital conversion and basic signal filtering are well-known technologies in the field and will not be elaborated upon here.

[0056] After completing the acquisition and preprocessing of the aforementioned microscopic physical feature data, the equipment linkage layer structures and packages the extracted absolute values ​​of motor current fluctuations, calibration reference current, actual acceleration, calibration reference acceleration, and abnormal vibration frequencies according to timestamps. The equipment linkage layer then synchronizes the structured microscopic physical feature dataset to the core allocation layer in real time via industrial Ethernet. This structured feature data, containing the underlying operating status of the equipment, is received by the core allocation layer and serves as the basic data input for subsequent execution of the equipment sub-health state prediction model.

[0057] After the device linkage layer completes the high-frequency acquisition of the underlying microscopic physical characteristics and synchronizes the feature dataset in real time, the core allocation layer performs the calculation of the device fault prediction model based on Logistic regression and the determination of sub-health status.

[0058] The core allocation layer receives structured data packets containing the absolute value of motor current fluctuations, calibration reference current, actual acceleration, calibration reference acceleration, and abnormal vibration frequencies. Traditional equipment scheduling systems allocate tasks based solely on whether the equipment is online, a binary state that cannot handle sudden downtime caused by equipment operating with defects. The core allocation layer utilizes continuous physical parameters to construct a continuous state assessment system, calculating the probability of equipment failure within a future wave cycle.

[0059] The core allocation layer utilizes an in-house deployed device failure prediction model to process the feature dataset. This prediction model is built on the Logistic Regression algorithm, and its specific mathematical expression is as follows: ; In the formula: This indicates the probability of a failure occurring in the target terminal execution device within a future wave cycle. This represents the absolute value of motor current fluctuations collected by the equipment linkage layer; Indicates the calibration reference current of the target terminal execution device; The relative frequency of abnormal fluctuations in motor current reflects the degree of power overload of the electrical drive system when overcoming track friction or abnormal internal resistance. This indicates the actual real-time acceleration during the initial phase captured by the sensor. This indicates the calibration reference acceleration issued by the motion control command; The degree of mechanical transmission efficiency decay measures the proportion of power response reduction caused by mechanical wear or aging of the transmission structure. This indicates the frequency of abnormal vibrations per unit time, reflecting the degree of physical deterioration of surface defects on the guide rail or loosening of gear components; , , These represent feature weight coefficients, used to adjust the contribution ratio of different microscopic observations to the overall equipment shutdown failure; This represents the system's basic environmental offset, used to compensate for the baseline deviation of sensor data caused by fluctuations in the physical environment of the storage site.

[0060] The core allocation layer executes the calculation engine, substituting the physical characteristic data into the above formula to obtain the failure probability value for continuous intervals. Subsequently, the core allocation layer obtains the dynamic early warning threshold stored in the rule engine and compares the calculated failure probability value with this dynamic early warning threshold for judgment. This early warning threshold is configured and calculated by the system based on the importance of the storage area and the fault tolerance rate of the current business time window.

[0061] When the calculated probability of failure is less than or equal to the warning threshold, the core allocation layer determines that the target terminal execution device and its associated roadway are in a healthy state. The system locates the device's identifier in the global device status mapping table and updates its status flag field to "generally available." This field attribute allows the system to issue a regular multi-target optimization allocation task to the device.

[0062] When the calculated probability of a fault exceeds the warning threshold, the core allocation layer determines that the target terminal execution device and its associated roadway have entered a sub-healthy state. The system skips the routine process of generating equipment shutdown commands and repair alarm work orders, locates the device's identifier in the global device status mapping table, and updates its status flag field to "restricted operation." This restricted operation status label is broadcast via the system's internal status bus, serving as a pre-triggered condition that drives the core allocation layer to initiate a predictive wave unbinding mechanism and a dynamic dimensionality reduction allocation coordination mechanism in subsequent stages. This establishes a direct link between the physical layer risk status of the equipment and the order matching logic at the business layer.

[0063] After the core allocation layer calculates the probability of device failure and updates the global device status mapping table, the system executes differentiated collaborative allocation strategies for devices and their associated available inventory for terminals with different status markers.

[0064] For target terminal execution devices marked as "generally available," the core allocation layer executes a standard multi-objective optimization allocation strategy. The core allocation layer retrieves the order queues from the allocation pool, sorted in ascending order of order priority. The system constructs an evaluation model based on multi-dimensional optimization objectives to match order demand with candidate inventory. These multi-dimensional optimization objectives specifically include the shortest path distance objective, the device load balancing objective, and the inventory space optimization objective.

[0065] The calculation logic for the shortest path distance objective is as follows: the system extracts the current physical coordinates of the target terminal execution device, the physical location coordinates of the candidate inventory, and the coordinates of the target outbound platform. It then calculates the total displacement path required for the device to complete the retrieval and transport the goods to the platform. The evaluation model tends to select the candidate location with the smallest total displacement path. The calculation logic for the device load balancing objective is as follows: the system calculates the current queue length of each regularly available device in real time. The evaluation model tends to allocate new orders to devices with shorter queue lengths to avoid congestion in localized storage areas. The calculation logic for the inventory space optimization objective is as follows: under the premise of fully satisfying multi-dimensional compliance checks, the evaluation model assigns higher selection weights to scattered locations with fewer storage units or those nearing their expiration date, thereby accelerating the clearing of fragmented warehouse space and releasing space. For the specific mathematical solution process of the multi-objective optimization evaluation model, those skilled in the art can use heuristic algorithms or linear programming algorithms to search for the optimal solution. The construction and solution algorithms of the multi-objective function are well-known technologies in this field and will not be elaborated here. The core allocation layer outputs the optimization calculation results, completing the matching and binding of healthy devices, available inventory, and outbound orders.

[0066] For sub-healthy devices marked as operating under restricted conditions, the core allocation layer triggers an anti-blocking collaborative defense mechanism. The pre-processing step of this defense mechanism is to perform predictive wave unbinding. Due to the extremely high risk of sudden downtime for sub-healthy devices, if these devices continue to execute complex wave orders containing a large number of independent SKUs, a physical shutdown would cause goods retrieved from other healthy devices within the complex wave to wait ineffectively in the merging sorting area for an extended period, leading to widespread wave deadlocks and sorting line blockages.

[0067] After receiving the restricted operating status tag of the device via the internal status bus, the core allocation layer actively retrieves the global task allocation queue. The system locates the wave of orders originally planned to be allocated to the corresponding lane of the sub-healthy device, as well as the pending orders for which inventory calculations are currently underway. The core allocation layer extracts the pre-calculated complexity dimension values ​​of these related orders.

[0068] The system has a preset order unbinding threshold. The core allocation layer compares the complexity dimension value of each extracted order with the unbinding threshold. When the complexity dimension value of an order is greater than or equal to the unbinding threshold, the system determines that the order is a high-complexity, multi-merging complex order. The core allocation layer immediately removes these complex orders from the task candidate pool of the sub-health device and unbinds them from the available inventory within the jurisdiction of the sub-health device.

[0069] After stripping and unbinding complex orders, the core allocation layer rescans the global available inventory pool. Within the lanes managed by healthy devices marked as generally available, the system searches for homogeneous redundant available inventory that meets the corresponding shipper's compliance and batch requirements. The core allocation layer reroutes these stripped high-complexity orders, allocating and binding them to the redundant inventory of healthy devices. This proactive wave unbinding process preemptively cuts off the path of underlying single-point hardware risk propagating to upstream complex business waves before physical equipment completely fails, achieving physical isolation between high-performance orders and potential failure nodes.

[0070] After the anticipated wave of debinding of sub-health equipment was completed, the core allocation layer further triggered dynamic dimensionality reduction allocation and collaboration logic. Since high-complexity orders have been stripped away, the sub-health equipment is in a business idle state, but there is still a large amount of available inventory within its managed physical aisles, and the equipment itself has physical operational capabilities. Directly shutting down the equipment would lead to excessive idleness of warehousing and logistics capacity.

[0071] The core allocation layer maintains a global pool of orders to be allocated within the system. The system introduces a dimensionality reduction threshold for order complexity within the core allocation layer. This threshold is dynamically configured based on the overall fault tolerance of the current warehousing operations and the total number of terminal execution devices in a normally available state. The core allocation layer retrieves the global pool of orders to be allocated and extracts the complexity dimension value of each unallocated outbound order within the pool, calculated during the preprocessing phase.

[0072] The core allocation layer compares the extracted complexity dimension values ​​with the order complexity dimensionality reduction threshold one by one. When the complexity dimension value of an outbound order is less than the order complexity dimensionality reduction threshold, the system marks the order as a low-complexity order. In terms of specific lower-level business characteristics, these low-complexity orders mainly include independent orders consisting of a single SKU, low-priority orders with a long time remaining before the fulfillment deadline, and regular orders that do not need to enter the downstream consolidation and sorting area for multi-item consolidation and packaging.

[0073] The core allocation layer extracts all low-complexity orders from the original sequence and constructs an independent, reduced-dimensionality allocation candidate queue. The system extracts the inventory demand characteristics of orders in the reduced-dimensionality allocation candidate queue and limits the target range to the physical lane area managed by the sub-healthy equipment, performing a targeted available inventory matching query. After confirming the existence of available inventory within the lane managed by the sub-healthy equipment that fully meets the order ownership and batch compliance requirements, the core allocation layer bypasses the global multi-objective optimization evaluation model and directly forces the assignment of these low-complexity orders to the sub-healthy equipment for execution.

[0074] The core allocation layer binds the pickup tasks of low-complexity orders to the physical storage locations within the aisles where sub-health equipment is located. The system generates a simplified outbound task package containing the specific physical storage location coordinates, equipment identification code, and SKU details, and stores it in the queue to be dispatched.

[0075] Through the aforementioned dimensionality reduction allocation logic, the core allocation layer enforces physical isolation of the internal structure of outbound orders at the business scheduling level. The system assigns equipment deemed to be in a sub-optimal state to handle only the simplest material handling tasks, thus utilizing the remaining available operating cycles of these sub-optimal devices while maintaining overall warehouse outbound throughput. Since all orders allocated to this restricted area are single, independent orders, they do not merge with the picking tasks of other equipment. This system cuts off the cascading propagation path of localized hardware risks across multiple devices, limiting the business impact of potential hardware failures to a single owner or SKU, providing structural defense support for outbound operations.

[0076] After the core allocation layer generates a complete order outbound allocation plan, the equipment linkage layer performs a low-level physical feasibility check and low-level scheduling confirmation on the allocation plan.

[0077] The core allocation layer integrates data from multi-objective optimization allocation tasks for healthy equipment areas and dynamic dimensionality reduction allocation tasks for sub-healthy equipment areas to generate a global outbound allocation plan. This global outbound allocation plan is then synchronously distributed to the equipment linkage layer, where the equipment control system resides, via an internal interface. The global outbound allocation plan includes the equipment identifier code bound to each task, the coordinates of the target physical storage location, and the estimated execution time window.

[0078] After receiving the global outbound allocation plan, the device linkage layer parses the instruction parameters in the task data packet. The device linkage layer extracts the job queue cache data for each target terminal execution device. The system reads the internally pre-configured maximum job capacity parameter for the terminal execution devices. This parameter limits the maximum queued task limit for various types of devices without physical deadlock. The device linkage layer counts the number of currently executing but incomplete legacy tasks for the corresponding device and adds this to the number of pending tasks assigned to that device in the newly issued allocation plan. The system compares this sum of task counts with the device's maximum job capacity. When the sum of task counts exceeds the maximum job capacity, the system determines that the device's job queue capacity verification has failed. The device linkage layer generates a capacity overload anomaly signal for the overflowing tasks.

[0079] For tasks that pass the work queue capacity verification, the equipment linkage layer further performs anti-collision verification of the physical operation path. In automated storage and retrieval systems (AS / RS), especially in scenarios involving multiple shuttles sharing the same level track or multiple aisles sharing the same hoist, concurrent tasks have a probability of physical space interference. The equipment linkage layer extracts the physical location coordinates of the starting point and the target exit coordinates of the task, and uses the underlying path control logic to calculate the three-dimensional motion trajectory curves of each terminal execution device participating in the operation. The system performs spatial intersection comparison of the motion trajectories of multiple devices within the same time window. If the system calculates that the three-dimensional motion trajectories of two or more devices overlap in the physical coordinate system at the same time node, the system determines that the physical operation path anti-collision verification has failed. The equipment linkage layer generates a path interference anomaly signal accordingly. For the three-dimensional spatial coordinate system establishment and motion trajectory planning algorithm of the underlying devices, those skilled in the art can use an anti-collision algorithm based on time window control for interference determination. The underlying trajectory calculation and avoidance logic are well-known technologies in the field and will not be elaborated here.

[0080] The device linkage layer aggregates all verification results and synchronously sends the verification feedback information back to the core allocation layer. For node tasks that fail verification, upon receiving a capacity overload or path interference anomaly signal, the core allocation layer forcibly disconnects the conflicting outbound orders from the matching available inventory. The core allocation layer returns these blocked orders to the allocation pool and re-optimizes inventory matching for them in the next business calculation cycle to avoid underlying physical congestion nodes.

[0081] For equipment tasks that pass verification, the core allocation layer officially freezes the corresponding physical inventory in the warehouse management system's accounting database. The system updates the status of these inventory entities to "order locked" to prevent other concurrent tasks from overselling this inventory. After locking the inventory data, the core allocation layer issues the final outbound operation execution instruction. The equipment linkage layer receives the instruction and converts it into electrical control messages, driving the underlying field terminals to move the equipment to the designated physical storage location to complete the physical retrieval and transportation operation.

[0082] After the warehousing operation execution instruction is issued, the core allocation layer and the equipment linkage layer work together to perform real-time status tracking and anomaly intervention on the underlying logistics operation process.

[0083] The equipment linkage layer continuously receives work status messages and location coordinate information from each terminal execution device. The system monitors the retrieval progress and equipment movement trajectory of each physical storage location. In actual physical operation scenarios, terminal execution devices may experience real shutdowns due to sudden mechanical jamming or electrical component failure. After arriving at the designated physical storage location, the terminal execution device detects that the storage location is empty through photoelectric sensors mounted on its front end, indicating a discrepancy between the actual stored goods and the recorded data.

[0084] When the equipment linkage layer detects a driver overload alarm code or a hardware shutdown signal such as communication heartbeat loss in the underlying control loop, or receives an empty storage location anomaly feedback code reported by the terminal execution device, the system determines that the outbound task execution of the current node is blocked. The equipment linkage layer packages the abnormal task execution status of the corresponding device and synchronizes it to the core allocation layer in real time.

[0085] Upon receiving an abnormal task execution status, the core allocation layer immediately triggers a dynamic reallocation mechanism. Because the system had already performed predictive wave unbinding and dynamic dimensionality reduction allocation operations during the initial calculation phase for devices predicted to be in a sub-optimal state, outbound tasks pre-assigned to the faulty node area were restricted to low-complexity orders. These low-complexity orders primarily consist of single-product structures and do not include cross-channel merging requirements or complex wave combination dependencies. This data structure isolation feature allows the system to handle sudden physical downtime without dismantling large merging waves.

[0086] The core allocation layer identifies all incomplete outbound tasks affected by equipment downtime or inventory discrepancies. The system forcibly unbinds these incomplete outbound tasks from their corresponding abnormal physical storage locations in the warehouse management system's accounting database, releasing the associated inventory lock status. For abnormal physical storage locations experiencing inventory discrepancies or blocked by faulty equipment, the system updates their status to "inventory frozen" and stops issuing new pickup or storage requests to those locations. Regarding concurrency control and task status rollback operations within the database, those skilled in the art can use standard transaction management mechanisms of relational databases to complete the underlying data processing. Data consistency rollback guarantees are well-known technologies in the field and will not be elaborated upon here.

[0087] The core allocation layer re-extracts low-complexity orders that have been unbound from the global allocation queue. The system scans the global device status mapping table for healthy devices in a normal available state. Within the physical area governed by the healthy devices, the core allocation layer retrieves homogeneous redundant available inventory that fully meets the ownership and batch compliance requirements of the blocked orders.

[0088] The core allocation layer invokes the system's built-in multi-objective optimization evaluation model to re-match these blocked, low-complexity orders with available physical storage locations and healthy terminal execution devices. The system regenerates the outbound allocation plan and executes it after completing a secondary verification of physical feasibility. This re-allocation logic leverages the low coupling characteristics of pre-allocation to avoid prolonged delays in the merging and sorting area for other outbound goods in related waves due to single-point hardware failures. Through rapid reorganization of the order structure and path re-optimization, the system transforms the uncontrollable hardware downtime risk at the lower level into a smooth system retry operation at the upstream business layer.

[0089] After all goods have been physically picked up and sorted, the core distribution layer executes the outbound accounting loop and ensures that the data is tamper-proof and traceable throughout the entire process.

[0090] The outbound goods arrive at the designated verification platform or packaging station. The on-site operation terminal uses a barcode scanner to collect the SKU barcode, batch code, and unique traceability code from the product's outer packaging. The operation terminal compares the collected physical data with the outbound order details issued by the core allocation layer. When the SKU type, actual quantity, and expiration date / batch of the physical goods are completely consistent with the order requirements, the operation terminal sends an outbound verification confirmation instruction to the core allocation layer.

[0091] Upon receiving the verification confirmation instruction, the core allocation layer executes a deduction operation in the accounting database of the warehouse management system. The system locates the corresponding physical location inventory record that was previously locked by an order and simultaneously deducts its total inventory and available quantity. After the deduction is complete, the core allocation layer generates a standardized outbound receipt message. This message includes the unique order number, details of the actual shipped SKU, actual quantity shipped, actual batch information, logistics carrier tracking number, and outbound completion timestamp. The core allocation layer then transmits the outbound receipt message synchronously to the upstream business layer's enterprise resource planning system and external e-commerce platform via system interfaces, achieving a closed-loop reconciliation of information flow between physical warehouse changes and corporate financial accounts.

[0092] While the accounting process is in closed loop, the core allocation layer initiates a full-process data traceability and archiving mechanism. The system extracts all the transaction data generated by the order throughout its entire lifecycle from the system operation logs. The extracted full transaction data includes the original order parameters issued by the upstream business layer, multi-dimensional compliance verification results, complexity dimension calculation values, sampling records of the underlying equipment's micro-physical characteristics, the probability of occurrence output by the equipment failure prediction model, the final bound terminal execution equipment identification code, and the actual underlying operation time.

[0093] The core allocation layer structures and concatenates the aforementioned full-volume data according to time series to construct a complete data traceability chain for a single order. To prevent malicious tampering or accidental overwriting of historical data in the background, the core allocation layer calls a cryptographic hash function to perform encrypted digest calculation on the entire data traceability chain. The system uses the calculated hash feature value as the digital fingerprint of the order data, and stores it together with the original traceability chain in a separately configured read-only audit database. For the specific implementation process of data hash digest calculation and read-only tamper-proof storage, those skilled in the art can use the SHA-256 algorithm combined with an immutable ledger database component for deployment. The underlying cryptographic operations and system-level read / write permission isolation control are well-known technologies in the field and will not be elaborated here.

[0094] Through the aforementioned tamper-proof traceability mechanism, the system tightly binds the status of business order flow with the underlying microscopic physical parameters of the equipment. In a cloud warehouse environment shared by multiple cargo owners, this data traceability chain provides underlying data support for cargo owner complaint review, inventory loss location, and liability determination for occasional equipment failures. This encrypted archived data, with deterministic execution results and underlying physical characteristics, also accumulates a historical sample library for the reverse calibration and adaptive iteration of various prediction models within the core allocation layer.

[0095] Based on the continuous operation of the system and the accumulation of actual operation data, the core allocation layer periodically performs adaptive reverse calibration of the feature weight parameters of the predictive model.

[0096] The core allocation layer relies on the aforementioned end-to-end data traceability and archiving mechanism and the actual operation logs collected by the equipment linkage layer to construct a historical sample set for model back-calibration. The core allocation layer extracts the actual operating status data of the terminal execution equipment within a set historical time window. The system extracts records of actual downtime anomalies or electrical faults that occur during the physical operation of the terminal execution equipment and marks them as positive samples, with a corresponding actual fault label value of one. The system extracts normal operation records where the equipment operates smoothly within the same time window without any shutdown alarms and marks them as negative samples, with a corresponding actual fault label value of zero. Corresponding to these actual fault labels is the low-level data frequently collected by the equipment linkage layer before the corresponding time node. This low-level data covers the absolute value of motor current fluctuations, actual acceleration, and abnormal vibration frequency. The low-level data and the actual fault labels together form a complete training sample sequence.

[0097] The core allocation layer inputs the training sample sequence into the back calibration module. The back calibration module extracts the predicted failure probability output by the Logistic Regression-based equipment failure prediction model at each sample time point. To quantify the deviation between the current predicted failure probability and the actual failure label, the back calibration module constructs a cross-entropy loss function. This cross-entropy loss function measures the overall prediction error rate of the prediction model under the current feature weight parameter configuration. Its specific mathematical expression is as follows: ; In the formula: This represents the cross-entropy loss value of the predictive model under the currently extracted training sample set; This represents the total number of training samples extracted. Indicates the extraction The prediction bias term for each training sample is calculated from the first sample to the last sample. The process of summing up each sample one by one; Indicates the first The actual fault label for each training sample is set to one if a fault occurs and zero if no fault occurs. The predictive model is for the first The predicted probability of failure is output from each training sample; This is the positive sample prediction bias, used to calculate the numerical bias caused by the actual occurrence of a fault but the lower probability of fault occurrence assigned in the early prediction. This is the negative sample prediction bias, used to calculate the numerical bias caused by assigning a higher probability of failure to a failure in the early predictions when no actual failure occurred.

[0098] After calculating the cross-entropy loss value, the reverse calibration module uses the gradient descent algorithm to iteratively update the feature weight factors within the prediction model. The system aims to reduce the cross-entropy loss value by calculating the partial derivatives of the loss function with respect to each weight factor and the base environment offset. The weight factors included in the aforementioned model formula specifically cover the acquisition weight factors. , , and system basic environment offset The reverse calibration module subtracts the product of the corresponding partial derivative and the preset learning rate from the original weight factors to obtain the updated calibration parameters. For the calculation of partial derivatives and the backpropagation differentiation process of multivariable functions, those skilled in the art can use the automatic differentiation algorithm module within the standard deep learning framework. The underlying matrix operations and gradient descent approximation strategies are well-known technologies in this field and will not be elaborated upon here.

[0099] After calculating a new set of calibration parameters, the reverse calibration module loads these updated parameters into the internal test environment and performs a secondary error calculation on the new model using the system's reserved independent validation sample set. The reverse calibration module compares the cross-entropy loss values ​​of the original and new model parameters on the independent validation sample set. When the loss value calculated by the new model parameters is lower than the corresponding loss value of the original model parameters, the core allocation layer confirms the validity of this reverse calibration. The system then synchronously updates these iteratively calibrated weight factors and environmental offsets to the rule engine in the production environment, completing the model parameter replacement.

[0100] By implementing the aforementioned closed-loop reverse calibration mechanism, the fault prediction model of the core allocation layer dynamically adjusts the calculation weight of various microscopic physical characteristic data collection items based on the actual wear and tear of the mechanical components of the storage equipment throughout their lifecycle and seasonal changes in ambient temperature and humidity. This adaptive parameter update process maintains the accuracy of the equipment sub-health status determination and ensures the effective triggering of the early predictive wave unbinding mechanism and the dynamic dimensionality reduction allocation coordination mechanism.

[0101] Specific application examples: Example: A specific application of an automated cloud warehouse for multi-owner daily consumer goods; Scenario Deployment: This example is applied to a high-concurrency automated cloud warehouse that undertakes e-commerce promotion business and is shared by multiple cargo owners.

[0102] Hardware configuration: Storage area equipment: An automated storage and retrieval system with 10 physical aisles, each aisle equipped with one high-speed stacker crane (numbered S01-S10). The main drive motor power supply circuit of each stacker crane is equipped with a high-precision current transformer, the chassis is equipped with a triaxial accelerometer, and the gear reducer is equipped with a piezoelectric vibration sensor.

[0103] Sorting area equipment: including cross-aisle merging conveyor lines and multi-station verification and packaging machines.

[0104] Control and computing nodes: edge computing gateway in the device linkage layer and computing cluster in the core allocation layer.

[0105] Business workflow and data interaction process: Step 1: Order parsing and complexity assessment. The system receives batch outbound orders from external e-commerce platforms.

[0106] Assessment: The core allocation layer parses order parameters and performs checks on ownership, total available inventory, and batches. For an order containing 15 different daily chemical products from 3 different suppliers and requiring urgent fulfillment, the system uses a complexity calculation formula to extract the number of SKU types, the total number of items, and priority weight coefficients for quantitative calculation. The system finds that its complexity dimension value far exceeds the preset upper limit and marks it as a high-complexity merged order. For another order containing only a single item of the same size tissue paper, the system calculates that its complexity dimension value is below the dimensionality reduction threshold and marks it as a low-complexity independent order.

[0107] Step 2: Low-level state perception and sub-health determination. After continuous operation, the bottom-level sensors of stacker crane S03 transmit microscopic physical characteristic data back in real time.

[0108] Sensing: Data extracted from the equipment linkage layer revealed that the absolute value of the motor current fluctuation of stacker crane S03 jumped from the usual 0.5 amps to 1.8 amps, while the frequency of abnormal vibrations per unit time also increased.

[0109] Calculation: The core allocation layer substitutes the above data into the equipment failure prediction model and calculates that the failure probability of stacker crane S03 in the next wave cycle reaches 75%.

[0110] Judgment: The system's dynamically configured warning threshold during the current promotional period is 60%. The system determines that the probability of a failure occurring is 75% beyond the safe range, and marks stacker crane S03 as being in a sub-healthy state, updating its underlying status label to restricted operation.

[0111] Step 3: Proactive Wave Unbinding and Dynamic Dimensional Reduction Allocation, Interception and Reorganization. The core allocation layer triggers the unbinding logic, scanning and identifying three types of candidate storage locations for high-complexity merged orders originally planned for allocation to lane S03. The system immediately unbinds these relationships and, within lanes S01 and S05 which are in a normally available state, re-matches homogeneous redundant available inventory for these three types of goods, completing the order reorganization.

[0112] Dimensional reduction and isolation: The system extracts low-complexity, independent orders such as single tissues from the global allocation pool and forcibly assigns them to stacker crane S03, which is in a restricted operating state. Stacker crane S03 only needs to complete a single-path reciprocating transport, severing its business dependency relationship with downstream complex wave confluence.

[0113] Step 4: Accounting closure and tamper-proof traceability, outbound and deduction, physical arrival at the verification platform, after the operation terminal scans the code to confirm that the product details are correct, the core allocation layer deducts the corresponding book inventory and generates an outbound receipt to be synchronized to the upstream enterprise resource planning system.

[0114] Source tracing on the blockchain: The system structurally combines the original order parameters, the calculated probability of failure, the final bound device identifier code S03, and the actual operation time. The core allocation layer calls the SHA-256 algorithm to generate hash feature values, which are stored in a read-only audit database, providing an immutable underlying basis for the reverse calibration of subsequent customer complaint tracing and predictive models.

[0115] Experimental verification and effect comparison: To verify the practical application effect of the present invention, a 30-day comparative experiment was conducted in the aforementioned multi-owner cloud warehouse environment.

[0116] Control group: A traditional load balancing scheduling system based on a single job queue length was used, without an underlying physical feature perception module or a complex order dimensionality reduction allocation mechanism.

[0117] Experimental group: The cloud warehouse outbound order allocation system based on the collaborative mechanism of equipment prediction model and dynamic dimensionality reduction allocation provided by this invention is activated.

[0118] Experimental data presentation: See attached document Figure 3 Chart description: (attached) Figure 3 This is a grayscale image. The axis labels and legends are displayed in Chinese. The horizontal axis represents the number of consecutive operating days (day 1 to day 30), the left vertical axis represents the average downtime of the equipment (minutes), and the right vertical axis represents the overall utilization rate of the equipment (percentage).

[0119] Data Interpretation: In the graph, the dashed line marked with triangles represents the downtime of the control group, while the solid line marked with circles represents the downtime of the experimental group. Because the control group failed to detect underlying physical problems, it continuously dispatched high-load combined tasks to the sub-healthy devices, resulting in several sharp spikes in downtime in the later stages (dashed line). The experimental group, through a dimensionality reduction allocation mechanism, reduced the mechanical operating intensity of the sub-healthy devices, effectively delaying the onset of physical failures. The average downtime shown by the solid line remains stable and low overall. The dotted line marked with diamonds, representing overall utilization, shows that the equipment utilization rate of the experimental group did not experience a precipitous drop due to the isolation mechanism, proving that the remaining lifecycle value of the sub-healthy devices was fully utilized.

[0120] See attached document Figure 4 Chart description: (attached) Figure 4 This is a grayscale image. The axis labels and legends are displayed in Chinese. The horizontal axis represents the number of experimental days (day 1 to day 30), and the vertical axis represents the frequency of wave deadlocks occurring in the merging and sorting area (times / day).

[0121] Data Interpretation: In the graph, the control group is represented by a dark gray dashed line with square markers, while the experimental group is represented by a thick black solid line with circles. During peak order periods, the control group experienced sudden physical failures of single-point equipment, causing other goods within the confluence flow to wait ineffectively on the sorting line for extended periods, resulting in extremely high frequency of flow deadlocks in the confluence area. The experimental group, due to its proactive flow unbinding and dimensionality reduction isolation allocation, completely avoided potential high-failure nodes for highly complex orders. The thick black solid line with circles representing the experimental group shows a stable downward trend.

[0122] Summary of Results Comparison: Sub-health status identification rate 0% (in a blind spot of surveillance) 94.5% (based on multidimensional physical characteristics) Identify and quantify underlying potential risks in advance Frequency of deadlock in waves 45 times / day on average Average 5 times / day Deadlock rate in core sorting area reduced by approximately 89%. Mean time downtime Average 120 minutes / day Average 52 minutes / day Overall unplanned downtime was reduced by approximately 56%. Throughput per hour 1850 orders / hour 2350 orders / hour Overall warehouse outbound operation efficiency improved by approximately 27%. Conclusion: Experimental results show that this invention achieves accurate perception of the sub-health status of warehousing equipment through high-frequency acquisition of underlying microscopic physical characteristics and predictive model calculation. The system, combined with predictive wave unbinding and dynamic dimensionality reduction allocation mechanisms, forcibly isolates high-dimensional business logic from underlying physical risk nodes before a complete physical hardware failure. This technical solution significantly eliminates wave deadlock in the merging sorting area and improves the system throughput and overall operation of multi-owner cloud warehouses when handling high-concurrency orders.

Claims

1. A cloud warehouse delivery order allocation method, characterized in that, Includes the following steps: The core allocation layer receives outbound orders from the upstream business layer, performs multi-dimensional compliance verification, quantifies the complexity dimension value, and generates the initial outbound wave pool. The core allocation layer constructs a full-dimensional inventory profile including physical location coordinates, and establishes a spatial mapping association between available inventory and terminal execution devices based on the physical location coordinates. The device linkage layer collects the microscopic physical characteristic data of the terminal execution device; The core allocation layer uses the equipment fault prediction model to calculate the probability of fault occurrence and makes a judgment: When the probability of a fault occurs is greater than the warning threshold, the terminal execution device is determined to be in a sub-healthy state. Based on spatial mapping association, predictive wave unbinding logic is executed to route orders originally planned to be allocated in the initial outbound wave pool and whose complexity dimension value is greater than or equal to the order unbinding judgment threshold to healthy devices. Dynamic dimensionality reduction allocation logic is executed to assign low-complexity orders less than the order complexity dimensionality reduction threshold to the terminal execution device, generating a global outbound allocation scheme. The equipment linkage layer performs a feasibility check on the global outbound allocation scheme. If the check passes, the core allocation layer freezes the corresponding inventory and issues an outbound operation execution instruction.

2. The cloud warehouse order distribution method according to claim 1, characterized in that, The core allocation layer performs multi-dimensional compliance checks, specifically including: Ownership of goods is verified by comparing the owner's identity in the outbound order with the ownership field in the inventory database. Calculate the total available inventory by deducting allocated locked inventory, pending inventory count frozen inventory, and loss frozen inventory from the total book inventory and then perform an inventory availability check. It also compares the batch number or production date specified in the order to perform a contract rule compliance check.

3. The cloud warehouse outbound order allocation method according to claim 1, characterized in that, The specific dimensions of the quantization computation complexity of the core allocation layer include: Extract the number of independent SKU types, total number of items, and basic priority weight coefficients for each order. Combine these with preset normalized weight coefficients to calculate the discrete pickup complexity component, handling scale complexity component, and fulfillment timeliness scheduling complexity component. Then, sum the components to obtain the complexity dimension value of the corresponding order.

4. The cloud warehouse outbound order allocation method according to claim 1, characterized in that, The core allocation layer establishes a spatial mapping association between available inventory and terminal execution equipment based on the physical location coordinate field, specifically including: Extract the warehouse area number, aisle number, row number, layer number, and column number of the physical location of the available inventory. Perform range matching in the pre-defined equipment topology table within the system to locate the target terminal execution equipment responsible for the three-dimensional boundary range of the physical space. Transform the available inventory in the business dimension into a candidate pool of equipment tasks in the physical dimension.

5. The cloud warehouse outbound order allocation method according to claim 1, characterized in that, The microscopic physical feature data collected by the device linkage layer specifically covers: The absolute value of motor current fluctuation, calibration reference current, actual acceleration during the start-up and deceleration phases, calibration reference acceleration issued by motion control commands, and abnormal vibration frequency during high-speed operation are obtained through sensors.

6. The cloud warehouse outbound order allocation method according to claim 5, characterized in that, The equipment fault prediction model is constructed based on the Logistic regression algorithm. The core allocation layer calculates the relative frequency of abnormal motor current fluctuations based on the absolute value of the motor current fluctuations and the calibration reference current, and calculates the degree of mechanical transmission efficiency attenuation based on the actual acceleration and the calibration reference acceleration. The above two items, together with the abnormal vibration frequency, are used as continuous state parameters. Combined with the feature weight coefficient and the system basic environment offset, the probability of fault occurrence of the output terminal execution equipment in the next wave cycle is calculated.

7. The cloud warehouse outbound order allocation method according to claim 1, characterized in that, The core allocation layer executes predictive wave unbinding logic, specifically including: Extract the complexity dimension value of the orders originally planned to be allocated to the terminal execution devices in the sub-healthy state from the initial outbound wave pool. When the complexity dimension value is greater than or equal to the order unbinding judgment threshold, the core allocation layer forcibly strips the corresponding order and searches for homogeneous redundant available inventory in the lanes managed by healthy devices in the normal available state to rematch and bind them.

8. The cloud warehouse outbound order allocation method according to claim 1, characterized in that, The core allocation layer executes dynamic dimensionality reduction allocation logic, specifically including: Orders in the initial outbound wave pool with a complexity dimension value less than the order complexity dimensionality reduction threshold are marked as low-complexity orders, extracted and constructed as an independent dimensionality reduction allocation candidate queue, and in the physical lane area managed by the terminal execution device in the sub-healthy state, a targeted available inventory matching query is performed, and the successfully matched low-complexity orders are forcibly assigned to the corresponding terminal execution device for execution.

9. A cloud warehouse outbound order allocation method according to claim 1, characterized in that, The feasibility verification of the global outbound allocation scheme performed by the device linkage layer specifically includes: The number of outstanding tasks currently being executed on each device is counted, and the number of tasks to be executed assigned to the corresponding device in the newly issued allocation plan is added together. The total number of tasks is then checked to determine whether the sum of the task counts exceeds the maximum work capacity. Extract the starting physical location coordinates and target exit coordinates of the relevant equipment, calculate the three-dimensional motion trajectory curve, determine the spatial overlap, and perform anti-collision verification of the physical running path. For tasks that fail the verification, the core allocation layer unlocks them and returns them to the allocation pool for re-optimization and matching of inventory.

10. A cloud warehouse outbound order allocation method according to claim 1, characterized in that, The process also includes adaptive reverse calibration after issuing the library job execution command. The core allocation layer relies on the actual operation logs collected by the device linkage layer to extract the downtime abnormal records of terminal execution devices within a set time window as positive samples and normal operation records as negative samples. Together with the high-frequency collected micro-physical feature data, they form a training sample sequence. The overall prediction error rate is calculated using the cross-entropy loss function, and the feature weight factors inside the device fault prediction model are iteratively updated through the gradient descent algorithm.

Images