An integrated intelligent footwear storage location management system

Abstract

This invention relates to the field of smart microgrid energy management technology and discloses an integrated smart footwear storage location management system, including a distributed resource storage module, a load flux coupling monitoring module, a dynamic virtual impedance generation module, and a load balancing scheduling control module. By monitoring the load flux density of logical partitions in real time to construct a nonlinear dynamic virtual impedance, and combining it with the correlation coupling degree model to calculate the comprehensive acceptance potential energy, load scheduling instructions are generated. This invention utilizes an embedded negative feedback saturation suppression loop to exponentially increase the impedance when the flux density approaches the threshold, forcibly suppressing the load adsorption capacity of high-heat nodes, and realizing dynamic power balance between energy flow and information flow within the system.

Description

Technical Field

[0001] This invention belongs to the field of intelligent warehousing and logistics technology, specifically relating to an integrated intelligent footwear storage location management system. Background Technology

[0002] Currently, in automated material handling and resource scheduling control systems, the core task of storage location allocation strategies is to establish a mapping relationship between logical data objects and physical storage nodes. Existing mainstream technologies typically employ open-loop control logic based on physical state feedback or static attribute partitioning. Specifically, the control system divides the physical storage space into several fixed logical functional areas and guides the units to be processed to specific areas based on their basic tag attributes. A traversal algorithm is used to find the first available free physical node to generate a storage instruction. This static rule-based discrete mapping mechanism can maintain basic space utilization and system throughput stability when processing single, standardized, low-frequency data streams, and is a common practice in the industry for building warehouse control systems. However, when processing high-frequency discrete data streams with strong correlation characteristics, the aforementioned static mapping mechanism... The system's topological defects have gradually become apparent. In specific complex application scenarios, the data objects input into the system often exhibit a nested dependency structure of sets and elements. Logically, a parent set identifies multiple child element units with strong symbiotic relationships. Existing control strategies often employ atomic discrete processing logic, ignoring the strong coupling relationships between child elements under the same parent set. This processing method leads to a spontaneous increase in entropy of the physical storage topology: high-frequency symbiotic elements belonging to the same set are randomly and disorderly dispersed and mapped to discontinuous physical nodes. When the system performs a retrieval or extraction task for this parent set, the execution mechanism is forced to make nonlinear long-distance round-trip movements between multiple nodes discretely distributed in physical space, resulting in redundancy in the operation path and a cumulative increase in the overall system response latency.

[0003] Static partitioning at the physical level has inherent limitations, and existing software control methods are insufficient to handle high-concurrency dynamic changes. For example, Chinese invention patent CN115860630B discloses an intelligent warehouse location management system. This system analyzes sales volume, purchase volume, and remaining stock of goods within fixed periods to construct a warehouse flow model and calculate flow coefficients. Based on this, it dynamically adjusts purchase volume and location allocation, combining this with risk assessment and optimization at storage height. While this solution addresses the overall location planning problem based on historical sales trends, it still relies on feedforward prediction using fixed-period historical statistical data. The optimization logic based on long-period statistical averages has inherent lag, making it difficult to capture the millisecond-level instantaneous pulse loads common in footwear warehousing scenarios, such as the release of popular items. Furthermore, the solution still treats each product as an independent discrete variable for risk assessment and quantification, lacking the ability to perceive the multi-dimensional, strongly coupled topological relationships specific to footwear colors and patterns. This paper addresses the bottleneck in execution efficiency caused by the physical discrete storage of related data from the underlying logic. Furthermore, existing technologies exhibit significant lag in handling the evolution of data object lifecycle states. Conventional optimization methods typically define static priority regions based on the statistical average of historical interaction frequencies and solidify physical resource allocation accordingly. However, the integrity state of data objects is highly time-varying. When a data set decays from a high integrity state to a fragmented state, if the system lacks dynamic perception capabilities, the fragmented set will continue to occupy the continuous physical address resources originally intended for high-throughput tasks. This results in high-value physical resources being locked by inefficient data units, creating a mismatch between resource attributes and data states. Simple physical expansion or periodic full defragmentation not only consumes enormous computing power and time costs but also fails to fundamentally resolve the inherent contradiction between dynamic data flows and static physical space mapping rules.

[0004] Therefore, the technical problem to be solved by this invention is how to construct a system that can perceive the integrity status and topology drift trend of a dataset in real time, and adaptively switch physical mapping strategies accordingly to achieve dynamic matching between the value of storage resources and the phase of the data lifecycle, thereby fundamentally suppressing system entropy increase and optimizing execution paths. Summary of the Invention

[0005] This invention provides an integrated intelligent footwear storage location management system, comprising:

[0006] The distributed resource storage module contains multiple power supply nodes that are logically mapped to have energy throughput capabilities, and each power supply node is divided into several logical control partitions.

[0007] The load throughput coupling monitoring module is used to collect real-time task throughput data of each power supply node and attribute feature vectors of the load units to be allocated, and to construct a correlation coupling degree model that characterizes the correlation strength between nodes based on historical load flow data.

[0008] The dynamic virtual impedance generation module is used to monitor the instantaneous load flux density of the logic control partition in real time, and execute nonlinear mapping logic based on the instantaneous load flux density to generate a dynamic virtual impedance value. This dynamic virtual impedance value defines the access resistance of the power supply node to the newly connected load unit.

[0009] The load balancing scheduling and control module is connected to the distributed resource storage module, the load flux coupling monitoring module, and the dynamic virtual impedance generation module. It is used to calculate the comprehensive acceptance potential of each available power supply node based on the difference between the gain value output by the correlation coupling degree model and the dynamic virtual impedance value, and to generate load scheduling instructions to control the load transmission execution module to conduct the load units to be allocated to the target power supply node with the largest comprehensive acceptance potential. Among them, the dynamic virtual impedance generation module has an embedded negative feedback saturation suppression loop. When the instantaneous load flux density of the logic control partition is detected to be close to the preset saturation threshold, the dynamic virtual impedance value is increased according to the preset exponential function relationship to suppress the load access capability of high correlation nodes and achieve system-level load flux balancing.

[0010] Preferably, in the dynamic virtual impedance generation module, the calculation logic of the dynamic virtual impedance value follows the following nonlinear exponential growth relationship: ,in, Let α be the dynamic virtual impedance value of the target power supply node k, β be the preset base impedance coefficient, β be the preset impedance sensitivity gain factor, and e be the base of the natural logarithm. The real-time normalized load flux density is the ratio of the task processing volume within a unit time window to the rated throughput capacity of the partition where the power supply node k is located.

[0011] Preferably, the load flux coupling monitoring module includes: a feature vector extraction submodule, used to parse the physical attribute data of the load units to be allocated and generate a unique feature correlation vector; an activity weighting submodule, used to obtain the historical access frequency data of each power supply node and convert it into the basic activity index of the node; and a coupling degree synthesis submodule, used to calculate the coupling coefficient between the feature correlation vector and the feature vector of the existing load units in each power supply node, and to weight and synthesize the coupling coefficient with the basic activity index to obtain the positive correlation gain value in the correlation coupling degree model.

[0012] Preferably, the load balancing scheduling control module includes a multi-dimensional potential energy differential calculation unit, which is used to perform the following operations: receiving the positive correlation gain value output by the load flux coupling monitoring module; receiving the dynamic virtual impedance value output by the dynamic virtual impedance generation module; performing a weighted differential operation on the positive correlation gain value and the dynamic virtual impedance value to obtain the net potential energy value characterizing the current acceptance capability of each power supply node; establishing a preferred queue, including power supply nodes with net potential energy values ​​higher than a preset reference level in the queue, and locking the node with the highest net potential energy value as the target power supply node.

[0013] Preferably, the system also includes an adaptive threshold drift control module connected to the dynamic virtual impedance generation module; the adaptive threshold drift control module is used to monitor the total load concurrency of the system, and when the total load concurrency exceeds the preset congestion warning line, it dynamically reduces the critical density threshold that triggers exponential impedance increase in the dynamic virtual impedance generation module, so as to start the shunting protection logic for high-throughput areas in advance.

[0014] Preferably, the distributed resource storage module also integrates a status feedback monitoring module, configured on each power supply node, for real-time detection of the node's occupancy status and the operational health of the underlying transmission execution logic; the status signal generated by the status feedback monitoring module is directly connected to the load balancing scheduling control module as a hard on / off constraint condition for calculating the comprehensive acceptance potential energy, wherein when the operational health is lower than the preset safety value, the load balancing scheduling control module forcibly sets the input impedance parameter of the node to infinity.

[0015] Preferably, the load balancing scheduling control module further includes a transient response smoothing filter; the transient response smoothing filter is used to normalize the real-time load flux density. Time-domain smoothing is performed to filter out data jitter caused by short-duration pulsed load requests, ensuring the dynamic virtual impedance value. The rate of change is within the preset system response bandwidth.

[0016] Preferably, the load transfer execution module includes automated transmission array logic and stacking execution unit; the load scheduling instruction includes transmission path planning data for the automated transmission array logic and action timing data for the stacking execution unit; the load balancing scheduling control module also includes a closed-loop status verification module, which monitors the actual displacement feedback of the load transfer execution module after the load scheduling instruction is issued, and triggers impedance reconstruction and path replanning when the feedback deviation exceeds the allowable range.

[0017] Preferably, the system further includes an energy efficiency optimization management module; this module is used to calculate the expected transmission energy consumption required to deliver the load units to be allocated to each candidate power supply node, and normalize the expected transmission energy consumption into an energy efficiency impedance component; when calculating the comprehensive acceptance potential energy, the load balancing scheduling control module superimposes the energy efficiency impedance component to achieve load allocation with the minimum total system energy consumption as a constraint.

[0018] Compared with existing technologies, the integrated intelligent footwear storage location management system of this invention has the following advantages:

[0019] 1. In intelligent footwear warehouse location management, by introducing phase control logic based on style completeness index into the decision-making unit, an adaptive allocation mechanism for physical storage resources is established. The system no longer follows a single static partitioning rule, but automatically switches between two physical mapping strategies—strong coherence aggregation and high entropy discrete state—based on the real-time coverage rate of data units belonging to the same set identifier. When the data set is in a high-completeness lifecycle, the system activates aggregation weights to lock continuous physical address blocks, ensuring the spatial compactness of high-frequency related data. When the completeness of the data set decays to below the discrete threshold, the aggregation constraint is automatically released, and the remaining data units are filled into non-continuous fragmented idle nodes as free particles. This binary phase modulation mechanism based on data state solves the structural contradiction in the storage system where continuous address resources are occupied by inefficient data. Without increasing the physical storage capacity, it maximizes the utilization rate of fragmented space and improves the effective data throughput efficiency per unit physical space.

[0020] 2. A topology-based randomization mechanism based on drift vector analysis is constructed to effectively overcome the technical defects in high-frequency interactive systems where data logical associations and physical location distributions become decoherent over time. The system uses the inbound signal triggered by reverse logistics as a passive calibration opportunity to calculate the physical centroid of the associated data cluster at the current moment in real time and detect the drift vector between the historical position of the unit to be processed and the current centroid. When the magnitude of the drift vector exceeds a preset threshold, the system generates a migration instruction pointing to the neighborhood of the new centroid, transforming necessary I / O operations into maintenance actions of the system's micro-topology. Without initiating additional large-scale defragmentation tasks, the disordered entropy increase of the physical storage topology is suppressed, ensuring that highly correlated data clusters always maintain a convergent and adsorbed state in physical space during dynamic flow, thereby maintaining the long-term stability of the system's indexing and access paths. Attached Figure Description

[0021] Figure 1 This is a logic block diagram of the dynamic load coupling monitoring and closed-loop scheduling of the present invention;

[0022] Figure 2This is a diagram of the offline / online collaborative deployment and parameter calibration architecture of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0024] It should be noted that all directional and positional terms used in this invention, such as: up, down, left, right, front, back, vertical, horizontal, inner, outer, top, bottom, transverse, longitudinal, center, etc., are only used to explain the relative positional relationship and connection between components in a specific state (as shown in the accompanying drawings). They are only for the convenience of describing this invention and do not require that this invention be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the descriptions of "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0025] In the description of this invention, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to the internal connection of two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0026] In the description of this specification, references to the terms "an embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0027] An integrated intelligent footwear warehouse location management system includes:

[0028] The distributed resource storage module contains multiple power supply nodes that are logically mapped to have energy throughput capabilities, and each power supply node is divided into several logical control partitions.

[0029] The load throughput coupling monitoring module is used to collect real-time task throughput data of each power supply node and attribute feature vectors of the load units to be allocated, and to construct a correlation coupling degree model that characterizes the correlation strength between nodes based on historical load flow data.

[0030] The dynamic virtual impedance generation module is used to monitor the instantaneous load flux density of the logic control partition in real time, and execute nonlinear mapping logic based on the instantaneous load flux density to generate a dynamic virtual impedance value. This dynamic virtual impedance value defines the access resistance of the power supply node to the newly connected load unit.

[0031] The load balancing scheduling and control module is connected to the distributed resource storage module, the load flux coupling monitoring module, and the dynamic virtual impedance generation module. It is used to calculate the comprehensive acceptance potential of each available power supply node based on the difference between the gain value output by the correlation coupling degree model and the dynamic virtual impedance value, and to generate load scheduling instructions to control the load transmission execution module to conduct the load units to be allocated to the target power supply node with the largest comprehensive acceptance potential. Among them, the dynamic virtual impedance generation module has an embedded negative feedback saturation suppression loop. When the instantaneous load flux density of the logic control partition is detected to be close to the preset saturation threshold, the dynamic virtual impedance value is increased according to the preset exponential function relationship to suppress the load access capability of high correlation nodes and achieve system-level load flux balancing.

[0032] Preferably, in the dynamic virtual impedance generation module, the calculation logic of the dynamic virtual impedance value follows the following nonlinear exponential growth relationship: ,in, Let α be the dynamic virtual impedance value of the target power supply node k, β be the preset base impedance coefficient, β be the preset impedance sensitivity gain factor, and e be the base of the natural logarithm. The real-time normalized load flux density is the ratio of the task processing volume within a unit time window to the rated throughput capacity of the partition where the power supply node k is located.

[0033] Preferably, the load flux coupling monitoring module includes: a feature vector extraction submodule, used to parse the physical attribute data of the load units to be allocated and generate a unique feature correlation vector; an activity weighting submodule, used to obtain the historical access frequency data of each power supply node and convert it into the basic activity index of the node; and a coupling degree synthesis submodule, used to calculate the coupling coefficient between the feature correlation vector and the feature vector of the existing load units in each power supply node, and to weight and synthesize the coupling coefficient with the basic activity index to obtain the positive correlation gain value in the correlation coupling degree model.

[0034] Preferably, the load balancing scheduling control module includes a multi-dimensional potential energy differential calculation unit, which is used to perform the following operations: receiving the positive correlation gain value output by the load flux coupling monitoring module; receiving the dynamic virtual impedance value output by the dynamic virtual impedance generation module; performing a weighted differential operation on the positive correlation gain value and the dynamic virtual impedance value to obtain the net potential energy value characterizing the current acceptance capability of each power supply node; establishing a preferred queue, including power supply nodes with net potential energy values ​​higher than a preset reference level in the queue, and locking the node with the highest net potential energy value as the target power supply node.

[0035] Preferably, the system also includes an adaptive threshold drift control module connected to the dynamic virtual impedance generation module; the adaptive threshold drift control module is used to monitor the total load concurrency of the system, and when the total load concurrency exceeds the preset congestion warning line, it dynamically reduces the critical density threshold that triggers exponential impedance increase in the dynamic virtual impedance generation module, so as to start the shunting protection logic for high-throughput areas in advance.

[0036] Preferably, the distributed resource storage module also integrates a status feedback monitoring module, configured on each power supply node, for real-time detection of the node's occupancy status and the operational health of the underlying transmission execution logic; the status signal generated by the status feedback monitoring module is directly connected to the load balancing scheduling control module as a hard on / off constraint condition for calculating the comprehensive acceptance potential energy, wherein when the operational health is lower than the preset safety value, the load balancing scheduling control module forcibly sets the input impedance parameter of the node to infinity.

[0037] Preferably, the load balancing scheduling control module further includes a transient response smoothing filter; the transient response smoothing filter is used to normalize the real-time load flux density. Time-domain smoothing is performed to filter out data jitter caused by short-duration pulsed load requests, ensuring the dynamic virtual impedance value. The rate of change is within the preset system response bandwidth.

[0038] Preferably, the load transfer execution module includes automated transmission array logic and stacking execution unit; the load scheduling instruction includes transmission path planning data for the automated transmission array logic and action timing data for the stacking execution unit; the load balancing scheduling control module also includes a closed-loop status verification module, which monitors the actual displacement feedback of the load transfer execution module after the load scheduling instruction is issued, and triggers impedance reconstruction and path replanning when the feedback deviation exceeds the allowable range.

[0039] Preferably, the system further includes an energy efficiency optimization management module; this module is used to calculate the expected transmission energy consumption required to deliver the load units to be allocated to each candidate power supply node, and normalize the expected transmission energy consumption into an energy efficiency impedance component; when calculating the comprehensive acceptance potential energy, the load balancing scheduling control module superimposes the energy efficiency impedance component to achieve load allocation with the minimum total system energy consumption as a constraint.

[0040] Preferably, the system is constructed as a hierarchical microgrid energy management architecture, wherein: the power supply node corresponds to the distributed energy storage unit in the energy distribution network; the load flux corresponds to the power flow in the energy distribution network; and the load balancing scheduling control module, as the energy management system, achieves dynamic power balance between physical material flow and information flow within the system by adjusting the dynamic virtual impedance value of each node.

[0041] Example 1: In a specific application scenario of the present invention, an integrated intelligent footwear warehouse location management system is used to process high-frequency concurrent footwear product inbound data streams. The system faces the data processing pressure of a massive amount of discrete unassigned load units (i.e., product inbound request data) concentrated into a specific logical area within a short time window. Each power supply node (physical location mapping data unit) in the distributed resource storage module is divided into multiple logical control partitions. At this time, one specific partition is in a high-load data throughput state due to association with popular product categories. When an unassigned load unit requests access, the load throughput coupling monitoring module collects the attribute feature vector of the load unit and calls historical load flow data to calculate the coupling coefficient between the feature vector and the feature vector of the existing load units in each logical control partition. Considering only the static association logic, since the load unit has a high-frequency co-occurrence characteristic with the existing inventory data in the high-load partition, the positive association gain value calculated by the module is at a high level. This value tends to index the load unit to the high-load partition.

[0042] Meanwhile, the dynamic virtual impedance generation module monitors the instantaneous load flux density of the high-load logic control partition in real time, and the monitoring data shows the real-time normalized load flux density of the partition. Once the congestion threshold is approaching (e.g., 0.9), the dynamic virtual impedance generation module then performs calculations based on an embedded nonlinear exponential growth formula, which is defined as follows: ;in, The dynamic virtual impedance value of the target power supply node k is given by α, which is a preset base impedance coefficient (e.g., set to 1.0), β, which is a preset impedance sensitivity gain factor (e.g., set to 5.0), and e, which is the natural logarithm base. This is due to the real-time normalized load flux density. The value is close to 1.0. The dynamic virtual impedance value generated through the above exponential function mapping calculation is... The non-linear, sharp increase quantifies the marginal resistance to data processing when a new task is connected to the node. The load balancing scheduling control module receives the positive correlation gain value and the dynamic virtual impedance value and performs multi-dimensional potential energy difference calculation. The module subtracts the dynamic virtual impedance value from the positive correlation gain value to calculate the net potential energy value of each power supply node in the hot spot area. The calculation results show that although the area has high correlation attributes, the high dynamic virtual impedance value offsets the correlation gain, and its final net potential energy value is lower than that of another non-hot spot partition with a lower real-time normalized load flux density. Based on this, the load balancing scheduling control module generates a load scheduling instruction pointing to the non-hot spot partition. Through the above data processing logic, without relying on the feedback delay of physical sensors, the system suppresses the data adsorption capacity of high-hot nodes through dynamic potential energy matching at the algorithm level, and realizes the dynamic power balance of energy flow and information flow within the system.

[0043] Example 2: This example verifies the throughput performance and congestion suppression capability of an integrated intelligent footwear storage location management system under high-concurrency load conditions. In particular, it quantitatively verifies the non-obvious technical effect of the synergistic effect of the dynamic virtual impedance generation module and the load flux coupling monitoring module. This experiment is based on a discrete event simulation platform. The core of this platform runs on a high-performance computing cluster to simulate the real physical environment of footwear logistics warehousing. The topology of the simulation environment is mapped from a standard footwear distribution center with 5,000 physical storage locations, which includes 10 logical control partitions. The input data stream used in the experiment comes from the historical real business order logs after anonymization, containing 100,000 discrete load unit (order line) requests. To ensure that the experiment covers non-ideal factors in engineering reality, Poisson distributed random impulse noise with a signal-to-noise ratio of 15dB is superimposed on the original order stream to simulate the real-time disturbance of the scheduling logic by sudden order cancellation and address change instructions.

[0044] Before the experiment began, the following deterministic calibration procedure was performed on the core parameters of the dynamic virtual impedance generation module: impedance sensitivity gain factor β and basic impedance coefficient α. First, parameter identification clarified that the β value determines the system's response rate to changes in load density. Second, technical trade-off analysis indicated that if the β value is too low, the system cannot generate sufficient impedance before congestion occurs; if the β value is too high, even small density fluctuations will trigger drastic impedance jumps, leading to scheduling oscillations. Third, based on the critical damping criterion in control theory, a judgment model was set: when the real-time normalized load flux density... When the warning threshold of 0.8 is exceeded, the dynamic virtual impedance value... The gain should be at least 10 times the average level of the positive correlation gain to achieve forced shunting. Finally, based on the above logic, through gradient pre-experimentation, the preferred parameter combination for this embodiment is determined to be: basic impedance coefficient α = 1.0, impedance sensitivity gain factor β = 5.0. For the engineering calibration of the impedance sensitivity gain factor β in the dynamic virtual impedance generation module, the system uses a synthetic load generator to inject a stepped increasing virtual task flow into the selected logic control partition during the non-operation window period, while monitoring the average task response delay data of the physical execution units in the partition. When the second derivative of the response delay with load density reaches the preset mutation threshold, the real-time normalized load flux density at this time is recorded. As the physical saturation critical point, and according to the formula Perform a reverse solution, adjusting the value of β to obtain the dynamic virtual impedance value at the critical point. To achieve a gain value 10 times higher than the average level of the positive correlation gain, the β parameter was determined to fit the performance boundary of the current physical warehouse hardware. To fully verify the technical effect, a gradient test system was constructed, including the sample group of this invention and three control groups: The sample group of this invention fully enabled the load flux coupling monitoring module and the dynamic virtual impedance generation module, with parameters set to α=1.0 and β=5.0; Control group A (existing technology / partially missing type) only enabled the physical space allocation logic based on static rules and removed the dynamic virtual impedance generation module (i.e., equivalent to β=0), aiming to verify the system performance without a congestion sensing mechanism; Control group B (overrange control / underdamped type) enabled the complete module, but set the impedance sensitivity gain factor to β=1.0 (below the lower limit of the preferred range), aiming to verify the rationality of the parameter boundary; Control group C (overrange control / overdamped type) enabled the complete module, but set the impedance sensitivity gain factor to β=10.0 (above the upper limit of the preferred range), aiming to verify the negative effect of overreaction.

[0045] After the test was started, a pulse load flow with a peak rate of 5000 units / hour was synchronously injected into each group of systems. In the first stage (0 to 30 minutes), the system was in a low load ramp-up period. Data shows that the average storage location response time of both the sample group of this invention and the control group A remained at around 12 seconds, indicating that under low-pressure conditions, the logic of this invention can smoothly adapt to traditional physical clustering strategies without introducing additional computational latency. In the second stage (30 to 90 minutes), the load flow entered a high-frequency concurrency peak period, and the instantaneous load throughput density of a specific popular logic control zone (Zone-H) rapidly increased. For control group A, monitoring data showed that the Zone-H... Sustained saturation at level 1.0 caused a linear accumulation of physical job queue lengths, worsening the average task waiting time from 12 seconds to over 450 seconds, leading to a severe resource deadlock. For control group B (underdamped type), although... The impedance generation logic was triggered, but because β=1.0, the generated impedance... Value is only The positive correlation gain from popular items (average 25.0) is far lower than that from popular items, failing to effectively offset the magnetic effect. This results in congestion still occurring in Zone-H, with a congestion relief rate of only 5%. For control group C (overdamped type), data shows that when... When it just touched 0.6, This drastic change caused the system to erroneously divert a large number of highly relevant orders to remote, less popular zones. While this eliminated congestion, it increased the average picking path length by 240%, sacrificing operational efficiency. For the sample group of this invention, when Zone-H... When it reaches 0.85, the system uses the formula The calculated dynamic virtual impedance value is approximately 70.1. This value, processed by the multi-dimensional potential energy difference calculation unit, instantly reverses the overall receiving potential energy of the region (the net potential energy value becomes negative). The system then generates scheduling instructions to smoothly guide subsequent high-heat loads to the secondary associated partition. Data shows that Zone-H's... Dynamically constrained to a steady-state level of 0.88, no saturation overflow occurred, and the average task waiting time remained stable within the range of 28 to 35 seconds. Experimental data showed that, when handling noisy high-concurrency loads, the sample group of this invention reduced the task congestion rate in hotspot areas by more than 85% compared to control group A, while maintaining the total throughput unchanged. Compared to out-of-range control groups B and C, the parameter range selected by this invention (β=5.0) achieved the optimal balance between congestion suppression and operational efficiency, confirming the synergistic effect between load throughput coupling monitoring and dynamic virtual impedance generation mechanism.

[0046] Example 3: This example focuses on the adaptive threshold drift control module and feature vector in an integrated intelligent footwear storage location management system. It extracts the internal working mechanism of the sub-module and discloses it in depth to clarify the dynamic adjustment logic of key control parameters and the vectorization processing procedure for unstructured data. During long-term system operation, when facing nonlinear network load fluctuations, fixed impedance generation parameters often fail to balance resource utilization under low load and congestion prevention strength under high load. The system needs to maintain dynamic balance through a built-in parameter adaptive drift mechanism. In the initial state definition phase, the system establishes the feature vector extraction... The input specifications for the submodule are as follows: the physical attribute data of the load units to be assigned are defined as a mixed feature set containing discrete variables (such as product category ID, season label) and continuous variables (such as size value, packaging volume). This submodule performs a deterministic vector mapping process: one-hot encoding is performed on all discrete variables to transform them into high-dimensional sparse vectors; the continuous variables are normalized to a zero-mean, unit-variance distribution; then, the above-processed data is concatenated and input into a pre-trained shallow fully connected neural network embedding layer, which contains a fixed weight matrix. The dimension is set to N×128, where N is the total dimension of the input features. Through matrix multiplication, the system outputs a fixed 128-dimensional dense real vector, which is then normalized using the L2 norm to generate a unique feature association vector. This step ensures that physical attributes of different dimensions are compressed into a unified metric space, providing a standardized mathematical foundation for subsequent coupling coefficient calculations. In the specific implementation of feature vector extraction, the feature vector extraction submodule converts discrete variables containing product category identifiers and seasonal tags into sparse vectors in one-hot encoded form, and concatenates them with normalized continuous variables of size and volume. The concatenated vector is then input into a preset weight matrix. The matrix multiplication operation is performed, and the resulting 128-dimensional dense real vector is normalized using the L2 norm. This vector is then determined as a feature correlation vector representing the physical attributes of the load units to be allocated. This process is achieved through a weight matrix. This module maps multidimensional unstructured attributes to a unified Euclidean space, ensuring a mathematical basis for quantitative comparison of different attribute features when calculating coupling coefficients. Following feature vector extraction, the activity-weighted submodule executes a time-based dynamic decay and negative feedback correction logic to calculate the basic activity index of each power supply node. This module does not simply accumulate historical access counts but introduces a time half-life mechanism and a job stall penalty factor, based on a recursive formula. The current activity level of node k is updated in real time; among which, The baseline activity level is the current activity level, and λ is a preset time decay constant, for example, set to λ = λ / 2. This is used to reflect the freshness weight of the data, where Δt is the time interval since the last update. This represents the number of access operations successfully completed within this time interval, where θ is the operation stall penalty coefficient, such as 2.0. The number of implicit negative events occurring at this node (including skips caused by path congestion or records of robotic arm grasping failures) is calculated. This calculation logic ensures that the metric can accurately reflect the true availability and popularity trend of the node at the physical operation level, preventing the task from being guided to pseudo-active nodes that are logically hot but physically faulty due to the reliance on historical high-frequency data. This provides a benchmark gain parameter with physical authenticity for the subsequent weighted synthesis of coupling coefficients.

[0047] Based on this, the adaptive threshold drift control module initiates real-time calibration of key parameters in the dynamic virtual impedance generation module, and the system periodically (e.g., every 500 milliseconds) collects the current total system load concurrency. This metric is defined as the ratio of the number of new task requests across the entire network within the current time window to the system's rated processing capacity. The control module internally runs a piecewise linear adjustment algorithm to dynamically correct the critical density threshold that triggers an exponential increase in impedance. When detected When in the low load range of 0 to 0.4, the system will Clamped at a reference value of 0.9, this allows the power supply node to generate high impedance only when near full load, maximizing the fill factor of the physical space; when When the load value climbs to the medium-high load range of 0.4 to 0.8, the module follows a negative correlation linear function. The threshold is adjusted in real time, where γ is a preset drift sensitivity coefficient (e.g., set to 0.5). This logic means that as global pressure increases, the system will proactively reduce its tolerance for local congestion and initiate diversion protection in advance. Ultimately, the adjusted critical density threshold... The threshold is injected into the nonlinear mapping logic of the dynamic virtual impedance generation module in real time. Specifically, this threshold acts as a shift parameter of the exponential function, causing the dynamic virtual impedance value to... The calculations are performed based on the updated relational formula. In a typical high-concurrency promotional scenario drill, as the overall network concurrency rate... The critical density threshold surged from 0.3 to 0.9, driven by the adaptive module. The parameter adjustment, which smoothly drifts from 0.9 to 0.65, causes the virtual impedance value generated in the hot spot area to increase exponentially in advance under the same local load flux density. Thus, before the physical congestion forms, the algorithmic high barrier construction guides the subsequent load to the less correlated but highly idle cold area. This process realizes a parameter adaptive compensation based on global state awareness, ensuring the control stability and logical closed-loop determinism of the system under full load conditions.

[0048] Example 4: To address the cold start data sparsity issue of the feature vector extraction submodule during initial system deployment, this example constructs and executes an offline pre-training and weight solidification procedure based on historical full-cycle data. Before system launch, anonymized historical business logs covering the complete seasonal fluctuation cycle are imported into an independent offline computing environment. The stochastic gradient descent algorithm is used to refine the weight matrix of the embedding layer. The system performs multiple rounds of iterative updates, using a triplet loss function as the objective constraint. The specific training sample construction rules are as follows: a product is randomly selected from the historical order database as the anchor sample; another product that appears in the same historical order as the anchor sample is selected as the positive sample; and a product that has never appeared in the same order as the anchor sample is selected as the negative sample. The training objective is to ensure that the Euclidean distance between the anchor sample and the positive sample in the 128-dimensional feature space is at least 0.5 units smaller than the distance between the anchor sample and the negative sample. The system continuously adjusts the weight matrix parameters until the proportion of samples satisfying this constraint exceeds 95%, and until the loss function value of the feature association prediction converges to a preset value. Within the accuracy tolerance, the final converged weight parameters are solidified into the system's initial configuration file, thereby ensuring that the system has a verified feature vectorization mapping capability at startup.

[0049] Meanwhile, to eliminate the nonlinear interference of physical environment differences on the control accuracy of the dynamic virtual impedance generation module, a standardized throughput stress test calibration procedure is performed during the field deployment phase. A stepped-incrementing virtual task request stream is injected into each logic control partition through a synthetic load generator, while simultaneously monitoring the real-time response delay data of the physical execution units. The critical frequency point where the second derivative of the delay curve abruptly changes is defined as the physical saturation boundary of that partition, and this boundary value is then locked as the normalized load flux density. The rated throughput capacity term in the calculation formula is used to accurately anchor the control algorithm to the load-bearing capacity of a specific physical site.

[0050] Example 5: This example discloses a field adaptive calibration procedure for the basic impedance coefficient α in a dynamic virtual impedance generation module. This addresses the potential response lag or oversensitivity issues that may arise from general parameter settings under varying warehouse physical layouts and hardware performance. The procedure defines a standard closed-loop debugging process to ensure that the optimal α value for the current environment can be determined through objective engineering steps after initial deployment or major hardware changes. During the initial state definition phase, the calibration procedure requires the system to be in a non-operational idle window and selects a representative logical control partition for calibration. The physical properties of the calibration partition must meet the standard normal distribution characteristics, i.e., it must contain various typical rack structures and be free of hardware faults. Simultaneously, it must ensure that the load throughput coupling monitoring module and the load balancing scheduling control module are in normal operating condition, and that relevant historical data has been cleared or archived to eliminate interference from historical weights. After the calibration process starts, the system initializes the basic impedance coefficient α to a conservative value of 0.1. Using the built-in load simulator, a constant virtual load flow equal to 80% of its rated throughput capacity is injected into the calibration partition. Under this steady-state load, the system continuously monitors the real-time task queue length of the partition. and average task waiting time If within 5 consecutive sampling periods (each period is, for example, 1 minute), If the impedance shows a monotonically increasing trend, it indicates that the current impedance value is insufficient to produce an effective shunt effect. The system will automatically increase the α value in steps of 0.1 and repeat the above monitoring process.

[0051] When detected Within a preset allowable range, such as maintaining a dynamic balance of 85% to 95% of the rated capacity, and If the preset Service Level Agreement (SLA) threshold is not exceeded, the system records the current α value. To verify the stability of this parameter, the system will instantly increase the virtual load flow to 120% of the rated capacity while keeping α constant, simulating a sudden congestion scenario. If the system can reduce the load within 3 sampling periods... If the convergence returns to the steady-state region, the current α value is determined to be the optimal basic impedance coefficient under this physical environment, and it is fixed as the running parameter of this partition. Conversely, if the convergence time exceeds the limit, α needs to be further fine-tuned or the physical bottleneck needs to be checked. This procedure eliminates the uncertainty of human experience setting through standardized stress testing and feedback adjustment closed loop, ensuring the reproducibility and control accuracy of impedance generation logic in real physical environment.

[0052] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. An integrated intelligent footwear storage location management system, characterized in that, include: The distributed resource storage module contains multiple power supply nodes that are logically mapped to have energy throughput capabilities, and each power supply node is divided into several logical control partitions. The load throughput coupling monitoring module is used to collect real-time task throughput data of each power supply node and attribute feature vectors of the load units to be allocated, and to construct a correlation coupling degree model that characterizes the correlation strength between nodes based on historical load flow data. The dynamic virtual impedance generation module is used to monitor the instantaneous load flux density of the logic control partition in real time, and execute nonlinear mapping logic based on the instantaneous load flux density to generate a dynamic virtual impedance value. This dynamic virtual impedance value defines the access resistance of the power supply node to the newly connected load unit. The load balancing scheduling and control module is connected to the distributed resource storage module, the load flux coupling monitoring module, and the dynamic virtual impedance generation module. It is used to calculate the comprehensive acceptance potential of each available power supply node based on the difference between the gain value output by the correlation coupling degree model and the dynamic virtual impedance value, and to generate load scheduling instructions to control the load transmission execution module to conduct the load units to be allocated to the target power supply node with the largest comprehensive acceptance potential. Among them, the dynamic virtual impedance generation module has an embedded negative feedback saturation suppression loop. When the instantaneous load flux density of the logic control partition is detected to be close to the preset saturation threshold, the dynamic virtual impedance value is increased according to the preset exponential function relationship to suppress the load access capability of high correlation nodes and achieve system-level load flux balancing.

2. The integrated intelligent footwear storage location management system according to claim 1, characterized in that, In the dynamic virtual impedance generation module, the calculation logic for the dynamic virtual impedance value follows the following nonlinear exponential growth relationship: ,in, Let α be the dynamic virtual impedance value of the target power supply node k, β be the preset base impedance coefficient, β be the preset impedance sensitivity gain factor, and e be the base of the natural logarithm. The real-time normalized load flux density is the ratio of the task processing volume within a unit time window to the rated throughput capacity of the partition where the power supply node k is located.

3. The integrated intelligent footwear storage location management system according to claim 1, characterized in that, The load flux coupling monitoring module includes: a feature vector extraction submodule, used to parse the physical attribute data of the load units to be allocated and generate a unique feature correlation vector; an activity weighting submodule, used to obtain the historical access frequency data of each power supply node and convert it into the basic activity index of the node; and a coupling degree synthesis submodule, used to calculate the coupling coefficient between the feature correlation vector and the feature vector of the existing load units in each power supply node, and to weight and synthesize the coupling coefficient with the basic activity index to obtain the positive correlation gain value in the correlation coupling degree model.

4. The integrated intelligent footwear storage location management system according to claim 1, characterized in that, The load balancing scheduling control module includes a multi-dimensional potential energy differential calculation unit, which performs the following operations: receiving the positive correlation gain value output by the load flux coupling monitoring module; receiving the dynamic virtual impedance value output by the dynamic virtual impedance generation module; performing a weighted differential calculation on the positive correlation gain value and the dynamic virtual impedance value to obtain the net potential energy value characterizing the current acceptance capability of each power supply node; establishing a preferred queue, including power supply nodes with net potential energy values ​​higher than a preset reference level in the queue, and locking the node with the highest net potential energy value as the target power supply node.

5. The integrated intelligent footwear storage location management system according to claim 1, characterized in that, The system also includes an adaptive threshold drift control module, which is connected to the dynamic virtual impedance generation module; The adaptive threshold drift control module monitors the total load concurrency of the system and dynamically reduces the critical density threshold that triggers an exponential impedance increase in the dynamic virtual impedance generation module when the total load concurrency exceeds the preset congestion warning line, so as to activate the shunting protection logic for high-throughput areas in advance.

6. The integrated intelligent footwear storage location management system according to claim 1, characterized in that, The distributed resource storage module also integrates a status feedback monitoring module, configured on each power supply node, for real-time detection of the node's occupancy status and the operational health of the underlying transmission execution logic; the status signal generated by the status feedback monitoring module is directly connected to the load balancing scheduling control module as a hard on / off constraint for calculating the comprehensive acceptance potential energy. When the operational health is lower than the preset safety value, the load balancing scheduling control module forces the input impedance parameter of the node to be set to infinity.

7. The integrated intelligent footwear storage location management system according to claim 2, characterized in that, The load balancing scheduling control module also includes a transient response smoothing filter; the transient response smoothing filter is used to normalize the real-time load flux density. Perform time-domain smoothing to filter out data jitter caused by short-term pulsed load requests.

8. The integrated intelligent footwear storage location management system according to claim 1, characterized in that, The load transfer execution module includes automated transmission array logic and stacking execution unit; the load scheduling instruction includes transmission path planning data for the automated transmission array logic and action timing data for the stacking execution unit; the load balancing scheduling control module also includes a closed-loop status verification module, which monitors the actual displacement feedback of the load transfer execution module after issuing the load scheduling instruction, and triggers impedance reconstruction and path replanning when the feedback deviation exceeds the allowable range.

9. The integrated intelligent footwear storage location management system according to claim 1, characterized in that, The system further includes an energy efficiency optimization management module; this module is used to calculate the expected transmission energy consumption required to deliver the load units to be allocated to each candidate power supply node, and normalize the expected transmission energy consumption into an energy efficiency impedance component; When calculating the overall receiving potential energy, the load balancing scheduling control module adds an energy efficiency impedance component.

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