Warehouse operation task scheduling system and method

Abstract

The present application provides a warehouse operation task scheduling system and method, which relates to the field of intelligent warehousing technology, including automatically generating replenishment tasks after completing the picking task, and integrating the center of gravity offset information of the inventory unit and the structural deformation information of the shelf to intelligently decide the shelving position and posture. The system obtains its modal response and identifies the deformation position and degree by deploying active micro-vibration devices and sensors at key parts of the shelf structure. At the same time, it performs three-dimensional modeling and center of gravity analysis on the inventory unit, evaluates the stability of different shelving schemes, and ensures safe placement. Further combining historical shelving information with mechanical models, the potential fatigue risk of the structure is predicted, and the shelf health information is dynamically updated. The system also supports intelligent fallback and reselection mechanisms when the storage location is unavailable, thereby improving the stability and flexibility of replenishment scheduling. The present application can be widely used in intelligent warehousing systems in high-density and complex material storage environments.

Description

Technical Field

[0001] The present application relates to the field of intelligent warehousing technology, and more specifically, to a warehouse operation task scheduling system and method. Background Art

[0002] With the rapid development of automated warehousing and intelligent logistics systems, task scheduling technology in warehouse operations has gradually evolved from static rule-driven approaches to dynamic perception and intelligent decision-making. Existing technologies typically generate replenishment tasks based on fixed rules or triggered by inventory thresholds, lacking the ability to dynamically perceive the actual state of the shelves. During the shelving process, the placement of inventory units (SIUs) is often determined by static location mapping or simple location optimization algorithms. These algorithms fail to consider the center of gravity offset characteristics of the SIUs and fail to dynamically respond to potential deformation risks of the shelf structure.

[0003] Furthermore, in existing warehousing systems, the structural health of shelves is typically inferred indirectly through manual inspections or long-term monitoring data, making it impossible to accurately assess and assess their status before replenishment tasks are executed. When inventory units have eccentric mass structures, or when the shelf structure exhibits deformations such as localized softening or loose connections, these can lead to unstable placement, potentially causing cargo to overturn, shelf deformation, or operational failure, posing significant safety risks.

[0004] Therefore, there is an urgent need for a task scheduling method and system based on dynamic center of gravity perception and structural state feedback to improve shelving accuracy and operation safety, and meet the dual needs of modern smart warehouses for high reliability and flexible scheduling. Summary of the Invention

[0005] In response to the deficiencies of the existing technology, the present application provides a warehouse operation task scheduling system and method.

[0006] In a first aspect, the present application provides a warehouse operation task scheduling method, comprising:

[0007] In response to the completion of the picking task for the target order, a replenishment task is generated based on the picking task and the preset replenishment rule, and is sent to the execution device; wherein the target order includes: inventory unit information of the expected picking;

[0008] Generating a replenishment task includes:

[0009] Based on the inventory unit information, determine a first shelving location of the inventory unit; obtain shelf health information of the first shelving location, wherein the shelf health information includes: shelf deformation information;

[0010] Obtaining center of gravity offset information of the inventory unit corresponding to the inventory unit information; adjusting the shelf position of the inventory unit on the shelf based on the center of gravity offset information and the shelf deformation information;

[0011] Active micro-vibration devices and sensors are arranged at the columns, beams and connection nodes of the shelf, wherein the active micro-vibration device is used to apply vibration excitation to the shelf, and the sensor is used to obtain the response signal of the shelf to the vibration excitation, and perform time-frequency analysis on the response signal to determine the deformation information of the shelf.

[0012] As an optional implementation manner, determining the first shelf location of the inventory unit based on the inventory unit information includes:

[0013] Based on the inventory unit information, determining whether the inventory unit has a fixed shelf location;

[0014] In response to the inventory unit having a fixed shelving location, selecting at least one location from a plurality of the fixed shelving locations as a first shelving location;

[0015] In response to the absence of a fixed shelving location for the inventory unit, total location status information is obtained, and based on the total location status information and the inventory unit information, locations that match the attributes of the inventory unit are determined, and at least one location is selected as the first shelving location.

[0016] As an optional implementation, when applying vibration excitation to the shelf and obtaining the response signal, the shelf is in an empty state;

[0017] The performing time-frequency analysis on the response signal to determine the shelf deformation information includes:

[0018] When the shelf is empty, applying pulse excitation to the columns or beams of the shelf through the active micro-vibration device;

[0019] Utilizing a plurality of sensors disposed at the columns, beams, and connection nodes of the shelf to collect corresponding response signals;

[0020] Performing time-frequency transformation on the response signal to obtain a multi-order modal distribution of the shelf;

[0021] Comparing the multi-order modal distribution with a pre-stored modal distribution under a healthy reference state to identify modal curve differences occurring in the horizontal or vertical positions of the shelf;

[0022] Based on the modal curve difference, the shelf deformation position is determined in the shelf space coordinate system.

[0023] As an optional implementation manner, after determining the deformation position of the shelf, the method further includes:

[0024] Performing a curvature difference operation on the modal curve corresponding to the deformation position of the shelf to obtain the modal curvature value at the deformation position of the shelf;

[0025] Performing differential calculation on the modal curvature value and the reference curvature value under a healthy reference state to obtain a deformation quantitative index;

[0026] The shelf deformation information is generated based on the deformation quantification index.

[0027] As an optional implementation manner, generating the shelf deformation information based on the deformation quantification index further includes:

[0028] Get the shelf's historical shelf information;

[0029] Based on the historical shelf information and the inventory unit information, obtaining a predicted deformation position and a predicted deformation degree of the shelf;

[0030] updating the shelf deformation position based on the predicted deformation position and the shelf deformation position;

[0031] The shelf deformation information is updated based on the predicted deformation degree and the shelf deformation information.

[0032] As an optional implementation manner, obtaining the center of gravity offset information of the inventory unit corresponding to the inventory unit information includes:

[0033] Reading the three-dimensional shape parameters and weight data of the inventory unit from the inventory unit information;

[0034] determining a geometric center of the inventory unit based on the three-dimensional shape parameters;

[0035] Calculating the actual center of mass position of the inventory unit in a three-dimensional coordinate system based on the weight data;

[0036] The center of gravity offset information is determined based on the actual mass center position and the geometric center.

[0037] As an optional implementation, the shelf deformation information includes: deformation position and deformation degree;

[0038] The adjusting the shelving position of the inventory unit on the shelf based on the center of gravity offset information and the shelf deformation information includes:

[0039] Determining an offset direction and an offset amount based on the center of gravity offset information;

[0040] Determine multiple candidate placement positions and placement postures within the placement range of the first shelf storage location;

[0041] Calculating the safety factor for each candidate placement position and placement posture based on the offset direction, offset amount, and deformation position, and ranking the candidate placement positions;

[0042] Select the candidate placement position and placement posture with the highest safety factor from the sorting results as the final shelf position;

[0043] The inventory unit is placed in the final shelf location.

[0044] As an optional implementation manner, after adjusting the shelving position of the inventory unit on the shelf based on the center of gravity offset information and the shelf deformation information, the method further includes:

[0045] In response to the inventory unit existing in the fixed shelving location and no matching shelving position in the first shelving location, selecting at least one location from the remaining plurality of fixed shelving locations as a second shelving location;

[0046] In response to the inventory unit not having the fixed shelving location and no matching shelving position in the first shelving location, at least one location is selected from the remaining multiple locations that match the attributes of the inventory unit as the second shelving location.

[0047] As an optional implementation manner, obtaining the predicted deformation position and predicted deformation degree of the shelf based on the historical shelf information and the inventory unit information includes:

[0048] Acquire the historical shelf information; wherein the historical shelf information includes: inventory unit information, shelf time, and center of gravity offset information corresponding to the inventory unit information in different time periods;

[0049] Based on the historical shelf information, statistically calculate stress accumulation parameters on the shelf;

[0050] Based on the inventory unit information and the stress accumulation parameter, using a preset shelf mechanics model, predicting the potential deformation position and generating corresponding predicted deformation coordinates;

[0051] According to the predicted deformation coordinates and the local stiffness parameters of the shelf, the predicted deformation degree corresponding to the predicted deformation position is calculated, and after comparing with the current shelf deformation information, the predicted deformation position and predicted deformation degree of the shelf are output.

[0052] In a second aspect, the present application provides a warehouse operation task scheduling system, comprising:

[0053] A computing device is configured to generate a replenishment task based on the picking task and a preset replenishment rule in response to completion of a picking task for a target order; determine a first shelving location for the inventory unit based on the inventory unit information; obtain shelf health information of the first shelving location; obtain center of gravity offset information of the inventory unit corresponding to the inventory unit information; and adjust a shelving location of the inventory unit on the shelf based on the center of gravity offset information and the shelf deformation information;

[0054] Sensors are placed on the columns, beams and connection nodes of the shelves to obtain response signals;

[0055] Active micro-vibration devices are installed on the shelves’ columns, beams, and connection nodes to apply vibration excitation to the shelves;

[0056] An execution device is used to execute the replenishment task.

[0057] Compared with the existing technology, this application can achieve a safer, more precise and more structurally adaptable shelving strategy by automatically generating a replenishment task after the picking task is completed, and fusing the center of gravity offset information of the inventory unit with the structural health information of the shelf before performing the replenishment operation. Compared with the existing technology that only relies on static location matching or rule-based location selection, this method introduces a shelf structure perception mechanism composed of active micro-vibration devices and sensors, and obtains the deformation information of the shelf in real time by performing time-frequency analysis on the response signal. At the same time, combined with the geometric modeling and mass center calculation of the inventory unit, the direction and amplitude of its center of gravity offset are evaluated, so as to dynamically adjust the placement position and posture of the inventory unit in the replenishment scheduling. This method improves the perception and response capabilities of the shelf structure status in warehousing operations, significantly enhances the stability and safety of shelving behavior, and effectively reduces the risks of overturning, abnormal force, etc. caused by structural deformation or unbalanced weight of items, and has higher adaptability and engineering practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A flow chart of a method for generating a replenishment task provided in an embodiment of the present application;

[0059] Figure 2 A schematic diagram of a warehouse operation task scheduling scenario provided in an embodiment of the present application;

[0060] Figure 3 A flow chart of a method for determining shelf deformation information provided in an embodiment of the present application;

[0061] Figure 4 A schematic diagram of a shelf deformation structure provided in an embodiment of the present application;

[0062] Figure 5 for Figure 4 A partial enlarged view of part A in the middle. DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0064] This application provides a warehouse operation task scheduling method, including:

[0065] In response to the completion of the picking task for the target order, a replenishment task is generated based on the picking task and the preset replenishment rule, and is sent to the execution device; wherein the target order includes: inventory unit information of the expected picking;

[0066] See also Figure 1 FIG. 1 is a flow chart of a method for generating a replenishment task provided in an embodiment of the present application, including steps S101 to S102, wherein:

[0067] S101: Determine a first shelving location of the inventory unit based on the inventory unit information; obtain shelf health information of the first shelving location, wherein the shelf health information includes shelf deformation information;

[0068] S102: Obtain the center of gravity offset information of the inventory unit corresponding to the inventory unit information; adjust the shelving position of the inventory unit on the shelf based on the center of gravity offset information and the shelf deformation information; wherein, sensors and active micro-vibration devices are arranged on the columns, beams and connection nodes of the shelf, which are used to obtain response signals by applying vibration excitation to the shelf, perform time-frequency analysis on the response signals, and determine the shelf deformation information.

[0069] This application is applicable to automated warehouse environments built on a warehouse management system (WMS) and an equipment control system (ECS). The method, executed by a scheduling control module, automatically generates replenishment tasks after a picking task is completed and intelligently determines the shelving location based on the shelf structure and the physical characteristics of the stock keeping unit (SKU).

[0070] When a target order recorded in the WMS completes its picking task, the system triggers the scheduling process and reads the inventory unit information associated with the order. Inventory unit information includes but is not limited to the following fields: unique number (unit_id), three-dimensional dimensions (size), weight (weight), material tag (material_tag), fixed slot flag (fixed_slot_flag), etc. The system determines whether to generate a replenishment task based on a preset set of replenishment rules. The replenishment rules may include strategies such as inventory lower limit triggering, replenishment upon picking completion, and scheduled replenishment. If the replenishment rules are met, a corresponding replenishment task object is generated and added to the WMS task queue. The task is then sent to the corresponding execution device, such as an automated guided vehicle (AGV) or a robotic arm, via the communication network.

[0071] After a replenishment task is generated, the system calls the location matching module to retrieve the status information of the currently available locations in the WMS. Each location object contains fields such as number, location, space dimensions, maximum load capacity, current load, shelf ID, and health status tag. If the inventory unit has a fixed shelving area, the system will select available and matching locations from the fixed shelving locations to form the first shelving location candidate set. If there are no fixed shelving locations, the system will match all available locations based on the inventory unit's attributes such as size, weight, and category to generate a candidate set, which will be recorded as the first shelving location.

[0072] To assess the structural condition of the racks at candidate locations, active micro-vibration actuators (AMAs) and accelerometer sensor nodes (ASNs) were pre-installed on the racks' main structural components. These devices were installed in the middle of beams, at the bottom of columns, and at key connection points to apply vibration excitation and collect structural response signals.

[0073] See Figure 2 , Figure 2 A schematic diagram of a warehouse operation task scheduling scenario provided in an embodiment of the present application;

[0074] The AMA and multiple ASNs are set on the shelves, and the AGV is responsible for the replenishment execution of the task.

[0075] During the health assessment process, the control system sends a start command to the AMA module via the Modbus TCP bus. The AMA module applies periodic swept-frequency or pulsed excitation to the structure within a set frequency range (e.g., 1 Hz to 2 kHz). The excitation duration is 2 to 3 seconds. The ASN module synchronously collects structural response signal data at a high-frequency sampling rate (e.g., 2 kHz) and transmits it via the bus to a central edge computing controller. This controller executes the time-frequency analysis module, which uses a short-time Fourier transform (STFT) or wavelet packet decomposition algorithm to perform frequency domain expansion on the response signal and extract the modal response characteristics of the rack structure.

[0076] The system differentially compares the currently measured modal frequency vector with the pre-stored healthy modal frequencies to identify structural locations with modal frequency shifts. This method determines the potential deformation areas of the shelf in the local coordinate system, forming the shelf's "structural health information" and linking it with the shelf's location information for subsequent shelving decisions.

[0077] At the same time, the system performs a gravity center offset analysis on the inventory unit to be replenished. First, the geometric analysis module is called to build a 3D model based on the 3D structure data and material weight data of the inventory unit. Calculated by the following formula:

[0078] ;

[0079] in, Represents the position vector of the geometric center of the inventory unit in the three-dimensional coordinate system (unit: meter), V is the total volume of the inventory unit (unit: ), is the spatial position vector of the volume integral variable. This formula is used to calculate the geometric center of an object with uniform density.

[0080] For inventory units with non-uniform mass distribution, the system introduces a density function , calculate the center of mass position :

[0081] ;

[0082] in, is the mass center vector (unit: meter), is the total mass (unit: kg), For location Mass density at (unit: ). This formula is the classical rigid body center of mass integral expression.

[0083] The system calculates the center of gravity offset vector for:

[0084] ;

[0085] in, Indicates the offset of the mass center relative to the geometric center. The system further converts it into polar coordinates ,in, They represent the horizontal angle, vertical inclination and offset modulus of the offset direction respectively.

[0086] Based on these two information sources, the system invokes the shelving location optimization module to screen and evaluate available locations within the first shelving location. By integrating the center of gravity offset vector with shelf structural health information, the system performs posture simulation and stability assessment on candidate placements, outputting a structural safety factor for each candidate. Ultimately, the shelving location and posture combination with the highest safety factor is selected as the final shelving solution for the inventory unit.

[0087] The task scheduling method provided in this embodiment can fully consider the shelf structure status and physical characteristics of the inventory unit on the basis of ensuring replenishment efficiency, and dynamically adjust the shelving behavior, thereby improving the operational safety and structural response adaptability of the warehousing system.

[0088] As an optional implementation manner, determining the first shelf location of the inventory unit based on the inventory unit information includes:

[0089] Based on the inventory unit information, determining whether the inventory unit has a fixed shelf location;

[0090] In response to the inventory unit having a fixed shelving location, selecting at least one location from a plurality of the fixed shelving locations as a first shelving location;

[0091] In response to the absence of a fixed shelving location for the inventory unit, total location status information is obtained, and based on the total location status information and the inventory unit information, locations that match the attributes of the inventory unit are determined, and at least one location is selected as the first shelving location.

[0092] In practice, the strategy for determining the first put-away location can be adjusted to account for the diversity of inventory units and the dynamic nature of warehouse resources. This strategy aims to address the issue of overly rigid or unresponsive put-away location assignment rules during replenishment tasks in existing warehouses. This strategy improves the flexibility and intelligence of put-away path planning while ensuring matching accuracy.

[0093] After the replenishment task is generated, the system calls the location matching module to process the inventory units currently to be replenished. First, the system reads the fixed location flag field (fixed_slot_flag) in the inventory unit information. If the value of this field is True, it means that the inventory unit has been bound to one or more exclusive fixed locations in the WMS. For example, specific types of items that need to be refrigerated or moisture-proof, or high-frequency SKUs that are partitioned for sorting convenience. In this case, the system filters out a set of currently idle locations from the fixed location pool, and selects them based on preset priority rules (such as the shortest transportation path, historical stacking stability score, etc.), and selects at least one idle location as the first shelving location.

[0094] If the inventory unit is not bound to a fixed shelving location, that is, the value of this field is False, the system enters dynamic matching mode. In this mode, the system first obtains the total location status information table (Slot State Table), which records the status parameters of all valid locations in the current warehouse, including three-dimensional dimensions, maximum load capacity, current remaining space, historical stacking frequency, current shelf number and corresponding structural health status identification. The system compares the main attribute fields of the inventory unit (size, weight, label) with the location parameters one by one. Through spatial compatibility judgment (that is, judging whether the size of the location is greater than the size margin of the inventory unit) and load-bearing capacity verification, the system selects free locations that meet the size and weight requirements as candidate sets.

[0095] Based on the candidate set, the system can also introduce a weighted scoring mechanism to comprehensively consider factors such as the length of the transport path, frequency of use, and shelf health to form a comprehensive scoring function. For example, the comprehensive scoring function can be:

[0096] ;

[0097] in, represents the comprehensive score of candidate storage location i; represents the size fit index of candidate location i (e.g., size matching margin); represents the handling convenience index of candidate location i (e.g., the inverse of the distance to the main channel); represents the current structural health score of candidate location i (e.g., set based on modal analysis results); 、 、 is the weight parameter, satisfying , which can be set according to the actual application strategy.

[0098] The system sorts all candidate storage locations in descending order based on the scoring results, and selects one or more storage locations with the highest scores as the first shelving location for processing by the subsequent shelving posture optimization module.

[0099] Through this implementation, when the system determines the first shelving location, it can fully utilize the preset fixed configuration strategy to ensure the rigid management requirements for special items, and dynamically adapt the current location resources without fixed constraints, thereby improving the location utilization rate and replenishment response speed, and further enhancing the flexibility, scalability and engineering practicality of the replenishment scheduling system.

[0100] As an optional implementation, when applying vibration excitation to the shelf and obtaining the response signal, the shelf is in an empty state;

[0101] See Figure 3 、 Figure 4 as well as Figure 5 , Figure 4 A schematic diagram of a shelf deformation structure provided in an embodiment of the present application; wherein portion A is the deformation area of the shelf.

[0102] Figure 5 for Figure 4 The enlarged view of the middle part A shows the deformation of the shelf A.

[0103] Figure 3 A flowchart of a method for determining shelf deformation information provided in an embodiment of the present application includes steps S201 to S205, wherein:

[0104] The performing time-frequency analysis on the response signal to determine the shelf deformation information includes:

[0105] S201: When the shelf is empty, applying pulse excitation to the columns or beams of the shelf through the active micro-vibration device;

[0106] S202: Collect corresponding response signals using multiple sensors arranged at the columns, beams, and connection nodes of the shelf;

[0107] S203: Performing time-frequency transformation on the response signal to obtain a multi-order modal distribution of the shelf;

[0108] S204: Compare the multi-order modal distribution with the modal distribution in a pre-stored healthy reference state to identify modal curve differences occurring in the horizontal or vertical position of the shelf;

[0109] S205: Determine the shelf deformation position in the shelf space coordinate system based on the modal curve difference.

[0110] To ensure the accuracy of shelf structural health assessments and improve the discernibility of modal response characteristics, this application prefers to perform vibration testing with the shelf empty. This approach aims to address modal parameter drift caused by cargo load interference and improve the repeatability and reliability of deformation identification results by establishing standardized testing conditions.

[0111] When a shelf is marked as "detectable" (i.e., no inventory units are currently stored), the system initiates the modal detection process. The control system issues a pulse excitation command to the AMA. In practice, the pulse excitation signal is a short, broadband pulse, typically set to 1 to 2 seconds in duration and with a frequency range of 1 Hz to 2 kHz. This pulse excitation is designed to stimulate multiple modal responses of the shelf structure. The excitation point is typically located in the middle of the shelf's columns or beams to improve excitation efficiency and avoid the influence of boundary conditions.

[0112] Multiple ASNs are pre-installed at key structural nodes of the racks, including the base of columns, the ends of beams, and at the horizontal and vertical connection points. These sensors synchronously collect response signals at a high sampling rate (e.g., 2kHz) and transmit them in real time to edge control computing devices via the CAN bus or industrial Ethernet.

[0113] After receiving the multi-point response signals, the central controller executes the time-frequency analysis module, performing short-time Fourier transform or wavelet packet decomposition on the signals. This extracts the frequency domain components of the multi-channel response signals and generates multi-order modal response curves for the rack structure. The modal distribution includes modal frequencies from the first to the fifth order (or higher) and the corresponding modal vibration amplitudes.

[0114] The system compares the modal distribution data obtained during the current period with the "modal distribution data under a healthy baseline state" pre-stored in the database. During this comparison, the system identifies areas of abnormal modal response in the rack structure, either horizontally (in the X-axis) or vertically (in the Z-axis), based on characteristics such as modal frequency shift amplitude, spatial phase difference of the mode shape curve, and changes in response energy distribution. This allows the system to determine the likelihood of localized deformation.

[0115] Finally, the system maps the spatial points corresponding to the identified modal curve differences to the local coordinate system of the shelf structure, determines the locations where the shelf may deform, and uses them as position items in the "shelf deformation information", and connects the data with the subsequent shelf stability calculation module.

[0116] Through this implementation, high-precision modal detection tasks can be carried out when the shelves are empty, eliminating the interference of dynamic loads on modal responses to the greatest extent, effectively improving the accuracy and stability of deformation identification, and providing more reliable structural health reference data for subsequent shelving optimization.

[0117] For example, in a high-density warehouse, deformation monitoring was performed on a set of standard light-load racks. The racks are four-tiered, consisting of steel columns, crossbeams, and latch connections. Each tier is 1.2m wide, 0.8m deep, and 2.0m high. The maximum load per tier is designed to be 300kg. To ensure accuracy, the inspection was performed at night when the racks were unloaded.

[0118] Eight triaxial accelerometers (ADXL356, sampling rate set to 2kHz) are installed in the middle of each shelf's two columns, at the ends of each of its four crossbeams, and at four key connection points. These sensors are connected to the edge acquisition controller via the CAN bus. Vibration excitation is applied by an electromagnetic active micro-vibration device (MVA-10) mounted at the center of the crossbeams. This device uses a single pulse excitation signal with a pulse duration of 0.5 seconds and a peak frequency of 1200Hz. This signal is uniformly triggered and issued by the controller.

[0119] After data acquisition, the controller preprocesses the eight response signals, including filtering, denoising, and normalization. It then uses a short-time Fourier transform to perform time-frequency expansion on each signal, extracting the first-, second-, and third-order main modal frequencies and amplitude characteristics for each sensor point. The extracted modal frequencies are: 135 Hz for the first order, 428 Hz for the second order, and 912 Hz for the third order.

[0120] The system compared the current modal frequency data with the modal baseline data recorded for this model of rack in its factory condition. It discovered a frequency drop of more than 10% near the first-order frequency at the beam connection node numbered "R2-N2," along with a significant asymmetry in the modal energy distribution. Based on this, the system determined that the beam in this area may be loose or the column twisted. The system marked the location of the abnormal area in the rack's local coordinate system at (X=0.6m, Z=1.2m).

[0121] The subsequent system will synchronously write the structural status identification of the abnormal area into the shelf health information field in the WMS, marking the area as "need to be avoided" for reference by the replenishment task scheduling system when performing shelf location screening to ensure that no heavy loading operations are performed on the area.

[0122] As an optional implementation manner, after determining the deformation position of the shelf, the method further includes:

[0123] Performing a curvature difference operation on the modal curve corresponding to the deformation position of the shelf to obtain the modal curvature value at the deformation position of the shelf;

[0124] Performing differential calculation on the modal curvature value and the reference curvature value under a healthy reference state to obtain a deformation quantitative index;

[0125] The shelf deformation information is generated based on the deformation quantification index.

[0126] In practice, after identifying potential deformation locations on the shelf, the system further performs curvature analysis on the modal response data corresponding to that location to quantitatively calculate the degree of deformation, thereby generating complete shelf deformation information. This implementation aims to address the technical flaw of existing modal monitoring systems, which only locate deformation but not quantify it, thereby improving the system's sensitivity to structural damage or plastic deformation and the accuracy of scheduling control.

[0127] After the controller completes the modal response frequency domain analysis and identifies the abnormal modal curve area, the system extracts the main modal response curve of the corresponding measuring point and sets the curve as the displacement response function , where x represents the spatial coordinate along the horizontal or vertical structural axis of the shelf (unit: m). The system performs a second-order difference operation on the curve near the deformation area to calculate its modal curvature value, which is expressed as follows: ;

[0128] in, Represents the modal curvature value at position x, in units of ; is the displacement response function, that is, the normalized displacement value of the vibration mode at this point; : The distance between adjacent sensors, in meters (m); the denominator Used to normalize spatial gradients to ensure physical consistency of curvature calculations.

[0129] The system retrieves the corresponding position modal response curve of this type of shelf in the healthy reference state from the database, which is recorded as , and calculate its curvature in the same way . Then, the system calculates the deformation quantitative index :

[0130] ;

[0131] in, It reflects the deviation of the modal curvature between the current structure and the healthy state, and is used to quantitatively judge the degree of structural deformation.

[0132] To improve robustness, the system can measure multiple adjacent measurement points at the deformation position. Perform sliding average or weighted average processing to generate the final deformation quantitative index Based on the comparison between the indicator value and the threshold (such as exceeding the 3σ standard deviation of the healthy state), the system identifies the location as a "structural abnormality point" and writes the deformation location coordinates and the corresponding quantitative indicators into the shelf deformation information field.

[0133] Through the above processing, not only can the spatial position of shelf deformation be accurately located, but the degree of deformation can also be generated on this basis, which can be used for subsequent safety factor estimation, scheduling avoidance path planning, maintenance alarm and other module calls.

[0134] For example, when further curvature analysis is performed on the abnormal area detected in the shelf above (coordinates are X=0.6m, Z=1.2m), the system selects the displacement data (normalized to unit) of the main modal response curve corresponding to this node and one adjacent node on its left and right (three points): ;

[0135] Substituting into the modal curvature formula: ;

[0136] Call the reference curvature value of the shelf model in the healthy reference state in the database , then the deformation quantification index is:

[0137] ;

[0138] If the curvature deviation control limit of this type of shelf structure in normal state is , the deformation quantitative index at this position exceeds the tolerance range, and the system generates the following structural health information entries based on this: deformation position: X=0.6m, Z=1.2m; deformation curvature difference: ; Deformation level: Medium (the system can set the level threshold).

[0139] This entry is written into the structural status database as shelf health information and is called by the subsequent shelving strategy adjustment module for dynamic avoidance and stacking posture correction strategies.

[0140] As an optional implementation manner, generating the shelf deformation information based on the deformation quantification index further includes:

[0141] Get the shelf's historical shelf information;

[0142] Based on the historical shelf information and the inventory unit information, obtaining a predicted deformation position and a predicted deformation degree of the shelf;

[0143] updating the shelf deformation position based on the predicted deformation position and the shelf deformation position;

[0144] The shelf deformation information is updated based on the predicted deformation degree and the shelf deformation information.

[0145] As an optional implementation manner, obtaining the predicted deformation position and predicted deformation degree of the shelf based on the historical shelf information and the inventory unit information includes:

[0146] Acquire the historical shelf information; wherein the historical shelf information includes: inventory unit information, shelf time, and center of gravity offset information corresponding to the inventory unit information in different time periods;

[0147] Based on the historical shelf information, statistically calculate stress accumulation parameters on the shelf;

[0148] Based on the inventory unit information and the stress accumulation parameter, using a preset shelf mechanics model, predicting the potential deformation position and generating corresponding predicted deformation coordinates;

[0149] According to the predicted deformation coordinates and the local stiffness parameters of the shelf, the predicted deformation degree corresponding to the predicted deformation position is calculated, and after comparing with the current shelf deformation information, the predicted deformation position and predicted deformation degree of the shelf are output.

[0150] Based on the current deformation position and modal curvature of the shelf obtained through real-time detection, the system further incorporates historical shelf information and mechanical prediction models to establish a prediction-calibration mechanism to improve the accuracy and timeliness of shelf deformation information. This implementation method primarily addresses the following issues: first, providing early warning for structural areas that currently exhibit no obvious modal anomalies; second, identifying potential long-term fatigue deformation based on historical load behavior; and third, enhancing the temporal continuity and dynamic response capabilities of shelf health information.

[0151] The system first calls the historical data module to extract the target shelf's historical shelf information from the structural health database. This historical shelf information includes at least the following fields: timestamp, shelf unit number, shelf duration, unit weight, 3D shape parameters, and center of gravity offset vector. By aggregating this information over time, it can be used to construct a comprehensive load distribution for different shelf locations over multiple time periods.

[0152] In engineering implementation, the shelf can be divided into several spatial cells (Voxel), and each shelf loading behavior is projected into the corresponding spatial cell to accumulate its equivalent stress impact. The system defines the "stress accumulation parameter" , represents the equivalent cumulative load intensity experienced by the shelf at position x, and is calculated as follows:

[0153] ;

[0154] in, : The number of inventory units at position x in the historical shelf records; : The weight of the inventory unit put on the shelf for the qth time (kg); : The shelf time of the qth inventory unit at this location (h); : The center of gravity offset coefficient of the qth shelf inventory unit, that is, the ratio of the offset vector modulus to the size, is used to characterize the torque effect; The unit is kg·h.

[0155] The system performs predictive analysis based on these stress accumulation parameters and a structural mechanics model of the rack. This model can be a parametric finite element model (FEM) or a neural network model trained on historical data, which simulates the stress-strain evolution path of structural nodes under long-term loads.

[0156] The system calls the prediction model interface, taking the current stress distribution map and the shelf's local stiffness parameters (which can be obtained through structural modeling or manufacturer specifications) as input. It then outputs the spatial coordinates of the shelf's potential deformation (predicted deformation location) and its corresponding predicted deformation value (predicted deformation degree). These prediction results constitute the "shelf predicted deformation information."

[0157] The system spatially matches and compares the predicted results with the actual deformation information obtained through real-time modal analysis. If the two are spatially adjacent or overlapping, the system uses a weighted fusion method to update the deformation position coordinates to improve positioning accuracy. If the predicted position is completely different from the detected position, the system marks the predicted location as a "warning area" and adds it to the avoidance candidate list for subsequent deployment strategies.

[0158] In addition, the system performs a fusion calculation of the predicted deformation degree and the modal curvature index obtained by detection, for example, using the exponential moving average (EMA):

[0159] ;

[0160] in is the final deformation quantitative index, is the actual measured deformation quantitative index, is the predicted deformation quantitative index, It is the fusion weight factor, which can be set according to the detection frequency and model confidence.

[0161] The resulting shelf deformation information includes: real-time measured modal curvature anomalies, predicted structural fatigue trend areas, and integrated quantitative deformation indicators. This information can be used to dynamically update shelf status labels, guide shelf location selection, or push maintenance tasks.

[0162] For example, the system performed a historical stress accumulation analysis on the right side of shelf number "R2-04." Retrieving the shelf loading records from the past 60 days revealed that this area had been carrying a type of metal component with a large center of gravity offset (center of gravity offset coefficient of approximately 0.18) every day, with an average weight of 43 kg and an average single dwell time of 26 hours.

[0163] The stress accumulation parameter of this area is calculated as:

[0164] ;

[0165] The system inputs this parameter into the shelf fatigue response prediction model (established based on the finite element regression curve), and the output results show that there is a potential deformation risk at the coordinate (X=0.95m, Z=1.6m), and the predicted curvature offset is .

[0166] Compared with the actual deformation quantitative indicators obtained in the early detection There is a high degree of spatial overlap, and the system performs weighted fusion processing and finally determines the degree of deformation of the area as , and update it to the high-risk area label of the current shelf, and mark the area into the list of prohibited areas for future replenishment task scheduling to avoid further expansion of structural overload.

[0167] As an optional implementation, to further improve the accuracy and acquisition efficiency of inventory unit center of gravity information, the system can use the following data modeling and algorithm implementation methods when performing center of gravity offset information calculation.

[0168] First, the 3D shape parameters contained in inventory unit information can be directly retrieved by the WMS from the material warehousing archive data, or obtained through automated measurement equipment such as structured light scanners, industrial X-ray CT devices, and laser ranging systems. Shape data is typically represented as a 3D mesh file in STL (stereolithography), OBJ, or PLY format. Within the edge computing platform, CAD parsing engines can be used to convert this data into a point cloud model or parametric geometry.

[0169] After the 3D model is built, the system uses the volume segmentation algorithm to discretize the entire structure. For example, the system uses the tetrahedral meshing method (Delaunay triangulation) to divide the inventory unit into N volume units. The spatial center of mass Perform ergodic integration to calculate the global geometric center:

[0170] ;

[0171] Among them, G is still the position vector of the geometric center of the inventory unit defined above in the three-dimensional coordinate system (unit: meter), that is, the geometric center vector of the entire inventory unit is obtained by integrating the volume-weighted centroid of each volume unit. Represents the position vector of the w-th small volume unit (grid unit), which is usually the geometric center of mass of the small volume unit (unit: meter). It should be noted that here The definition of does not conflict with the definition of r above. In actual engineering implementation, the system discretizes the integral volume V into space, and uses the geometric center position vector of each volume unit as Approximately represents the spatial position vector r within the volume integration region to complete the numerical transformation from integration to summation.

[0172] The mass data can then be obtained through the embedded weighing module or the standard weight field in the historical database. If the material of the inventory unit is not evenly distributed, the system can call its material hierarchy and combine the mass density of each substructure to obtain the mass data. and geometric volume Perform weighted integration to calculate the actual center of mass:

[0173] ;

[0174] in, is the total mass, Represents the local mass density corresponding to the w-th volume unit (unit: kilograms per cubic meter, ). In addition, in actual engineering implementation, the system uses a spatial discretization method for the three-dimensional model to convert the mass center integral calculation into a weighted summation of local volume units. However, the physical meaning of the obtained mass center vector C is consistent with the integral definition above, and the unit is meter (m). Finally, through the vector difference:

[0175] ;

[0176] The system obtains the center of gravity offset vector, whose modulus is Indicates the offset magnitude, and the direction is determined by the vector direction. The system converts the vector into polar coordinates ( ), used for unified calling of downstream modules.

[0177] To optimize system efficiency, the system can cache calculated center of gravity offset information in the inventory unit attribute field, allowing it to be reused in subsequent dispatches, avoiding redundant modeling and repeated integral calculations. When multiple similar inventory units exist, the system can also introduce representative modeling and parameter fitting mechanisms to reduce modeling costs.

[0178] It should be noted that, in order to facilitate implementation in actual engineering systems, the system performs spatial discretization on the three-dimensional model of the inventory unit, and uses volume segmentation and weighted summation instead of theoretical integration to achieve numerical calculation of the geometric center and mass center.

[0179] As an optional implementation, the shelf deformation information includes: deformation position and deformation degree;

[0180] The adjusting the shelving position of the inventory unit on the shelf based on the center of gravity offset information and the shelf deformation information includes:

[0181] Determining an offset direction and an offset amount based on the center of gravity offset information;

[0182] Determine multiple candidate placement positions and placement postures within the placement range of the first shelving location;

[0183] Calculating the safety factor for each candidate placement position and placement posture based on the offset direction, offset amount, and deformation position, and ranking the candidate placement positions;

[0184] Select the candidate placement position and placement posture with the highest safety factor from the sorting results as the final shelf position;

[0185] The inventory unit is placed in the final shelf location.

[0186] Furthermore, before the SKU is shelved, the system dynamically adjusts its specific placement and posture on the shelf, combining its center of gravity offset information with the structural deformation information of the shelf corresponding to the first selected shelving location. This improves overall shelving stability and structural response safety. This approach primarily addresses the problem of leveraging the SKU's eccentricity and the shelf's localized flexibility for refined positioning optimization, avoiding the risk of structural overload, tipping, or localized extrusion.

[0187] In a specific implementation, the system first offsets the vector from the center of gravity of the inventory unit Extract its offset direction (polar coordinate angle ) and offset Combined with the structural health data of the shelf area corresponding to the target storage location, the deformation position coordinates and their corresponding deformation degree (such as modal curvature difference or predicted softening factor) are obtained to form the shelf deformation information set.

[0188] The system then creates a set of candidate locations within the placement range of the first shelving location. To this end, the system spatially discretizes the placement area and generates several candidate points for placement in the horizontal (X), vertical (Y) and layer height (Z) directions of the shelf with a fixed step spacing. Combined with the placement methods allowed for each inventory unit (such as bottom-down, inverted, sideways, etc.), each candidate placement position is assigned one or more acceptable placement postures to construct a set of candidate shelving solutions. :

[0189] ;

[0190] in, : The spatial coordinates of the T-th candidate placement point in the shelf coordinate system, in meters (m); : The rotation angle of the jth candidate posture around the X axis (horizontal axis), in degrees (°), indicating the flipped or supine state; : The rotation angle of the jth candidate posture around the Z axis (vertical axis), in degrees (°), indicating the direction; is the total number of candidate placement points, is the total number of candidate poses, the combined set of listing solutions Total Candidate listing options.

[0191] In order to quantitatively compare the candidate racking schemes, a structural safety factor evaluation formula is designed based on the physical moment balance principle, which is defined as follows:

[0192] ;

[0193] in, : Structural safety factor (dimensionless) of the candidate solution (T, j). The larger the value, the more stable it is. : The mass of the inventory unit (kg), obtained from the inventory record or weighing module; : Gravitational acceleration constant, take ; : The horizontal distance (m) from the candidate point to the load-bearing center of the shelf (the geometric center of the support surface), reflecting the support strength; : The offset distance (m) between the center of gravity and the support surface in the current posture. The larger the offset, the higher the risk of overturning. : The softening factor of the shelf structure corresponding to the candidate placement position, which indicates the degree of stiffness attenuation of the local structure under excitation. The unit is Newton meter (N m) and can be calculated by the modal curvature difference or modal frequency offset.

[0194] The above formula couples the center of gravity shift trend with the structural response characteristics of the shelf to form a set of shelf stability evaluation system for dynamic structural states. It is considered unsafe when , the position is considered to have a high degree of stable redundancy.

[0195] The system calculates all candidate solutions And sort in descending order, and finally select the combination with the highest safety factor As the final shelf position and posture of the inventory unit, the equipment control system sends instructions to the AGV or robotic arm to complete the shelf action.

[0196] Through this implementation, the stacking stability of inventory units under the constraints of the shelf structure can be significantly improved, and the physical risks caused by the superposition of center of gravity offset and structural deformation can be minimized, thereby enhancing the engineering robustness and safety control capabilities of the automatic replenishment system.

[0197] For example, a smart warehouse needs to precisely stack a batch of eccentric cylindrical chemical reagent bottles. The mass of this type of inventory unit is 8.5kg, the height is 0.4m, the center of gravity relative to the center axial offset is 0.09m, and the direction is the upper front side of the bottle mouth. Through the above-mentioned center of gravity modeling module, the system obtains its offset vector , offset direction , .

[0198] The system obtains the first shelf location of the inventory unit as the lower left area of shelf "R3-02" and retrieves the deformation information of the shelf from the modal analysis module. It identifies that there is a structural softening area at the coordinate (X=0.3m, Z=0.4m) with a high degree of deformation. The system generates 36 sets of position-posture combination schemes within the candidate range and calculates the safety factor for each set of schemes. It finds that when some postures are offset in the direction toward the deformation area, their In the same direction as the flexibility, the safety factor is less than 1.5.

[0199] Finally, the system selects a set of rotations The final inverted placement solution places the weight away from the deformation zone and closer to the center of gravity of the support surface, achieving a safety factor of 3.8. The system records this position and posture and uses it as the final shelving instruction, completing precise placement via the AGV platform.

[0200] As an optional implementation manner, after adjusting the shelving position of the inventory unit on the shelf based on the center of gravity offset information and the shelf deformation information, the method further includes:

[0201] In response to the inventory unit existing in the fixed shelving location and no matching shelving position in the first shelving location, selecting at least one location from the remaining plurality of fixed shelving locations as a second shelving location;

[0202] In response to the inventory unit not having the fixed shelving location and no matching shelving position in the first shelving location, at least one location is selected from the remaining multiple locations that match the attributes of the inventory unit as the second shelving location.

[0203] After the system adjusts the shelving position based on the center of gravity offset and shelf deformation information, if it finds no matching placement position or posture available in the current first shelving location, the system will initiate a dynamic fallback process to further search for other candidate locations to ensure smooth replenishment and avoid structural risk stacking. This approach primarily addresses the issue of how the system can adapt to re-route and expand its selection space when, in actual operation, the current recommended location cannot meet stable shelving conditions due to severe local shelf deformation, drastic center of gravity offset, or other uncontrollable factors.

[0204] In a specific implementation, the system attempts to perform a safety assessment in the first put-to-shelf location (see the safety factor above). After the calculation process of , if the safety factors of all candidate placement positions and posture combinations are lower than the lower limit threshold set by the system (such as ), the system will consider that there is no matching shelving location for the current first shelving location and trigger the fallback logic.

[0205] The system first determines whether the current inventory unit has a fixed shelving location (i.e., its fixed_slot_flag=True). If so, the system removes the currently unsuccessful locations from the predefined set of fixed locations and re-applies the first shelving location selection logic (see the matching process above) to the remaining fixed locations, forming a second set of candidate shelving locations. The system then re-invokes modules such as shelf health analysis, center of gravity offset calculation, and safety scoring to complete the next round of shelving location decision-making.

[0206] If a stock keeping unit does not have a fixed shelving location, the system enters general location matching mode. From the remaining available locations that match the stock keeping unit's attributes, the system eliminates the first location that has been tried and generates a new set of candidate locations based on criteria such as space size, load capacity, availability, and shelf health. This set of locations serves as the second shelving location. The location optimization process is then repeated in the same manner.

[0207] To avoid dead loops or frequent rollbacks, the system can set a maximum number of attempts, progressive rollback rules, or task termination criteria (such as sending a manual intervention flag after three consecutive failures). The entire rollback mechanism is implemented as a modular process within the system architecture, supporting dynamic loading and parallel calls to ensure stable and responsive replenishment tasks.

[0208] Through the introduction of this implementation method, the system has the ability to flexibly adapt to real-world unpredictable working conditions, improving the engineering robustness of the scheduling logic and the task success rate. It is particularly suitable for large-scale inventory management and smart warehousing scenarios with frequent fluctuations in structural health status.

[0209] Based on the same inventive concept, the embodiments of the present application also provide a warehouse operation task scheduling system corresponding to a warehouse operation task scheduling method. Since the principle of solving the problem of the system in the embodiments of the present application is similar to the above-mentioned rule-based generation-based logistics billing engine method in the embodiments of the present application, the implementation of the system can refer to the implementation of the method, and the repeated parts will not be repeated. The system includes:

[0210] A computing device is configured to generate a replenishment task based on the picking task and a preset replenishment rule in response to completion of a picking task for a target order; determine a first shelving location for the inventory unit based on inventory unit information; obtain shelf health information of the first shelving location; obtain center of gravity offset information of the inventory unit corresponding to the inventory unit information; and adjust a shelving location of the inventory unit on the shelf based on the center of gravity offset information and the shelf deformation information;

[0211] Sensors are placed on the columns, beams and connection nodes of the shelves to obtain response signals;

[0212] Active micro-vibration devices are installed on the shelves’ columns, beams, and connection nodes to apply vibration excitation to the shelves;

[0213] An execution device is used to execute the replenishment task.

[0214] Exemplary computing devices, such as industrial edge computing units, integrated servers, or scheduling master control modules, are used to execute scheduling control logic. Specific functions include: triggering the replenishment process based on the task completion signal after the target order picking task recorded by the WMS is completed; reading inventory unit information and calling the replenishment rule set to generate replenishment tasks and assign them to the execution queue; calling the location matching logic to determine the first shelving location based on inventory unit attributes and location status; calling the modal recognition algorithm module to obtain the current structural status of the shelf and generate shelf deformation information; executing the center of gravity calculation module to extract the center of gravity offset of the inventory unit geometric model and mass data; combining the center of gravity information with the shelf deformation information to perform shelving position optimization and posture simulation, and output the final shelving instruction.

[0215] For example, sensors are used to acquire shelf structural response signals. This component consists of multiple highly sensitive vibration response sensors (such as accelerometers and MEMS inertial modules) deployed at key structural nodes of the shelf, including columns, beams, and horizontal and vertical connections. Its functions include: acquiring multi-channel response signals at a high sampling rate during excitation; transmitting the signals in real time to a computing device for time-frequency analysis and modal parameter extraction; and monitoring shelf status changes over the long term to support predictive maintenance and health history modeling.

[0216] For example, active micro-vibration devices are deployed at the structural nodes of the shelf, in conjunction with sensors. These devices can be electromagnetic actuators, piezoelectric drive modules, or other devices capable of short-duration pulse or swept-frequency excitation. Their functions include: receiving control commands from the dispatching system and applying control excitation when the shelf is unloaded or within a specified time window; the excitation frequency range covers the first to third-order modal response range of the shelf structure; and working in conjunction with sensors to achieve high-precision modal identification and local flexibility inference.

[0217] For example, an execution device is used to specifically execute replenishment tasks and shelving operations. This device may include, but is not limited to, an automated guided vehicle (AGV), a robotic arm, a stacker, and a pallet lift. Its functions include receiving replenishment task instructions and shelving parameters issued by a computing device; accurately completing the transportation and placement of inventory units from pickup to designated locations; and supporting collaboration with the WMS and ECS to achieve closed-loop task execution and status feedback.

[0218] Modules exchange data via industrial buses (such as CAN and Modbus TCP) and wireless communication protocols (such as Wi-Fi 6 or LoRa), and are coordinated by unified system management software. The system can run on a local server or an edge cloud platform, and features remote upgrades, module reconfiguration, and algorithm strategy adaptation.

[0219] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

Claims

1. A warehouse operation task scheduling method, characterized in that: include: In response to the completion of the picking task for the target order, a replenishment task is generated based on the picking task and the preset replenishment rule, and is sent to the execution device; wherein the target order includes: inventory unit information of the expected picking; Generating a replenishment task includes: Based on the inventory unit information, determine a first shelving location of the inventory unit; obtain shelf health information of the first shelving location, wherein the shelf health information includes: shelf deformation information; Obtaining center of gravity offset information of the inventory unit corresponding to the inventory unit information; adjusting the shelf placement position of the inventory unit on the shelf based on the center of gravity offset information and the shelf deformation information; the shelf deformation information includes: deformation position and deformation degree; The adjusting the shelving position of the inventory unit on the shelf based on the center of gravity offset information and the shelf deformation information includes: Determining an offset direction and an offset amount based on the center of gravity offset information; Determine multiple candidate placement positions and placement postures within the placement range of the first shelving location; Calculating the safety factor for each candidate placement position and placement posture based on the offset direction, offset amount, and deformation position, and ranking the candidate placement positions; Select the candidate placement position and placement posture with the highest safety factor from the sorting results as the final shelf position; placing the inventory unit in the final shelf position; Active micro-vibration devices and sensors are arranged at the columns, beams and connection nodes of the shelf, wherein the active micro-vibration device is used to apply vibration excitation to the shelf, and the sensor is used to obtain the response signal of the shelf to the vibration excitation, and perform time-frequency analysis on the response signal to determine the deformation information of the shelf.

2. A warehouse operation task scheduling method according to claim 1, characterized in that: The determining, based on the inventory unit information, a first shelf location of the inventory unit includes: Based on the inventory unit information, determining whether the inventory unit has a fixed shelf location; In response to the inventory unit having a fixed shelving location, selecting at least one location from a plurality of the fixed shelving locations as a first shelving location; In response to the absence of a fixed shelving location for the inventory unit, total location status information is obtained, and based on the total location status information and the inventory unit information, locations that match the attributes of the inventory unit are determined, and at least one location is selected as the first shelving location.

3. A warehouse operation task scheduling method according to claim 1, characterized in that: When applying vibration excitation to the shelf and obtaining a response signal, the shelf is in an empty state; The performing time-frequency analysis on the response signal to determine the shelf deformation information includes: When the shelf is empty, applying pulse excitation to the columns or beams of the shelf through the active micro-vibration device; Utilizing a plurality of sensors disposed at the columns, beams, and connection nodes of the shelf to collect corresponding response signals; Performing time-frequency transformation on the response signal to obtain a multi-order modal distribution of the shelf; Comparing the multi-order modal distribution with a pre-stored modal distribution under a healthy reference state to identify modal curve differences occurring in the horizontal or vertical positions of the shelf; Based on the modal curve difference, the shelf deformation position is determined in the shelf space coordinate system.

4. A warehouse operation task scheduling method according to claim 3, characterized in that: After determining the deformation position of the shelf, the method further includes: Performing a curvature difference operation on the modal curve corresponding to the deformation position of the shelf to obtain the modal curvature value at the deformation position of the shelf; Performing differential calculation on the modal curvature value and the reference curvature value under a healthy reference state to obtain a deformation quantitative index; The shelf deformation information is generated based on the deformation quantification index.

5. A warehouse operation task scheduling method according to claim 4, characterized in that: The step of generating the shelf deformation information based on the deformation quantification index further includes: Get the shelf's historical shelf information; Based on the historical shelf information and the inventory unit information, obtaining a predicted deformation position and a predicted deformation degree of the shelf; updating the shelf deformation position based on the predicted deformation position and the shelf deformation position; The shelf deformation information is updated based on the predicted deformation degree and the shelf deformation information.

6. A warehouse operation task scheduling method according to claim 1, characterized in that: The obtaining of the center of gravity offset information of the inventory unit corresponding to the inventory unit information includes: Reading the three-dimensional shape parameters and weight data of the inventory unit from the inventory unit information; determining a geometric center of the inventory unit based on the three-dimensional shape parameters; Calculating the actual center of mass position of the inventory unit in a three-dimensional coordinate system based on the weight data; The center of gravity offset information is determined based on the actual mass center position and the geometric center.

7. A warehouse operation task scheduling method according to claim 2, characterized in that: After adjusting the shelf position of the inventory unit on the shelf based on the center of gravity offset information and the shelf deformation information, the method further includes: In response to the inventory unit existing in the fixed shelving location and no matching shelving position in the first shelving location, selecting at least one location from the remaining plurality of fixed shelving locations as a second shelving location; In response to the inventory unit not having the fixed shelving location and no matching shelving position in the first shelving location, at least one location is selected from the remaining multiple locations that match the attributes of the inventory unit as the second shelving location.

8. A warehouse operation task scheduling method according to claim 5, characterized in that: The obtaining of the predicted deformation position and predicted deformation degree of the shelf based on the historical shelf information and the inventory unit information includes: Acquire the historical shelf information; wherein the historical shelf information includes: inventory unit information, shelf time, and center of gravity offset information corresponding to the inventory unit information in different time periods; Based on the historical shelf information, statistically calculate stress accumulation parameters on the shelf; Based on the inventory unit information and the stress accumulation parameter, using a preset shelf mechanics model, predicting the potential deformation position and generating corresponding predicted deformation coordinates; According to the predicted deformation coordinates and the local stiffness parameters of the shelf, the predicted deformation degree corresponding to the predicted deformation position is calculated, and after comparing with the current shelf deformation information, the predicted deformation position and predicted deformation degree of the shelf are output.

9. A warehouse operation task scheduling system, used to implement a warehouse operation task scheduling method according to any one of claims 1 to 8, characterized in that: include: a computing device for generating a replenishment task based on the picking task and a preset replenishment rule in response to completion of the picking task for the target order; Determine the first put-to-shelf location of the inventory unit based on the inventory unit information; Obtaining shelf health information of the first shelving location; Obtaining center of gravity offset information of the inventory unit corresponding to the inventory unit information; Adjusting the shelving position of the inventory unit on the shelf based on the center of gravity offset information and the shelf deformation information; Sensors are placed on the columns, beams and connection nodes of the shelves to obtain response signals; Active micro-vibration devices are installed on the shelves’ columns, beams, and connection nodes to apply vibration excitation to the shelves; An execution device is used to execute the replenishment task.

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