Intelligent warehousing scheduling method and device

Abstract

The application discloses an intelligent warehouse scheduling method and device, relates to the technical field of intelligent warehouse logistics, and is applied to a scheduling system and comprises the following steps: acquiring target goods, determining the actual weight of the target goods through the vehicle-mounted controller of an automatic guided vehicle, the current temperature of the goods and the lifting height; acquiring the actual weight and the target goods ID, screening the goods storage positions according to the size relationship between the actual weight and a target threshold value, and obtaining a candidate storage position set; determining the target score of the candidate storage positions based on the deviation change quantity and the balance change quantity of each storage position after the target goods are placed in the candidate storage positions, and determining the target storage position based on the target score; sending a carrying instruction to the automatic guided vehicle based on the target storage position and the target goods ID, so as to carry the target goods to the target storage position based on the carrying instruction; and determining the position and weight record information of the target goods, so that the automatic guided vehicle acquires the goods based on a taking instruction and completes the warehousing. The safety and stability of the storage system are ensured.

Description

Technical Field

[0001] This invention relates to the field of intelligent warehousing and logistics technology, and in particular to an intelligent warehousing scheduling method and device. Background Technology

[0002] In modern high-density warehousing, the allocation strategy of racking locations directly affects storage security, operational efficiency, and rack lifespan. Current mainstream racking location allocation methods suffer from the following main drawbacks: Static allocation strategies cannot adapt to dynamically changing actual loads: Existing systems typically allocate storage locations once upon goods arrival, based on their preset theoretical weight or volume. However, in actual operation, the combination of goods on the same pallet may change, and there may be a discrepancy between the theoretical and actual weights. This static, estimation-based allocation method cannot perceive the true weight of goods in real time, leading to a significant deviation between the actual shelf load and the system model, posing a risk of overloading.

[0003] Lack of real-time optimization for the overall load-bearing balance of the shelving: Traditional methods (such as categorizing goods and storing them at the nearest warehouse) primarily optimize access paths, neglecting the mechanical balance of the shelving as a whole structure. Long-term uneven storage of goods can lead to: Structural stress concentration: Long-term local overloading of the shelving accelerates metal fatigue, leading to deformation or even the risk of collapse.

[0004] Uneven ground pressure: This causes uneven pressure on the warehouse floor, which affects the stability of the foundation in the long term.

[0005] Increased wear and tear on storage and retrieval equipment: AGVs (Automated Guided Vehicles) or stacker cranes operating in unevenly loaded aisles experience unbalanced forces, accelerating wear on tires and drive components.

[0006] Potential safety hazards of "top-heavy, bottom-light": If heavy objects are placed in high-level storage locations due to inaccurate system information or human error, the center of gravity of the shelving will be significantly raised, reducing overall stability and significantly increasing the risk of overturning in the event of an earthquake or accidental impact.

[0007] The contradiction between storage space utilization and security: To ensure security, existing systems often use very conservative load-bearing estimates, resulting in the underutilization of rack load-bearing capacity and hidden waste of storage space.

[0008] Therefore, how to maximize the utilization of warehouse space while fundamentally ensuring the long-term security and stability of the storage system is an urgent problem to be solved. Summary of the Invention

[0009] In view of this, the purpose of this invention is to provide an intelligent warehouse scheduling method and apparatus capable of making real-time, optimal warehouse location allocation decisions, thereby maximizing warehouse space utilization while fundamentally ensuring the long-term security and stability of the storage system. The specific solution is as follows: In a first aspect, this application discloses an intelligent warehouse scheduling method, applied to a scheduling system, comprising: Upon receiving an inbound task, the system acquires the target goods using an automated guided vehicle (AGV) based on the inbound task, and determines the actual weight of the target goods using the AGV's onboard controller, the current temperature of the goods, and the lifting height. Obtain the actual weight and target cargo ID uploaded by the automated guided vehicle, and filter the cargo storage locations according to the relationship between the actual weight and the target threshold to obtain a set of candidate cargo locations; The target score of the candidate storage location is determined based on the deviation change and balance change after the target goods are placed in each storage location in the candidate storage location set, and the target storage location is determined based on the target score. Based on the target storage location and the target cargo ID, a handling instruction is sent to the automated guided vehicle (AGV) so that the AGV can move the target cargo to the target storage location based on the handling instruction. Upon receiving an outbound task, the location and weight of the target goods are determined based on the outbound task, and a pickup instruction is sent to the automated guided vehicle (AGV) so that the AGV can retrieve the goods based on the pickup instruction and complete the outbound process.

[0010] Optionally, before determining the actual weight of the target cargo using the onboard controller of the automated guided vehicle, the current cargo temperature, and the lifting height, the method further includes: A pressure sensor is installed in the hydraulic circuit of the forklift mast lifting cylinder or at the fork root of the automated guided vehicle. Determine the unloaded reference value when the automated guided vehicle is unloaded and stationary in a preset horizontal calibration zone.

[0011] Optionally, determining the actual weight of the target cargo using the onboard controller of the automated guided vehicle, the current cargo temperature, and the lifting height includes: Determine the sensor value of the pressure sensor when the automated guided vehicle is loading the target cargo; Determine the difference between the sensor value and the no-load reference value; The target system gain in the pre-built system gain table is determined based on the current cargo temperature and lifting height; the system gain table is constructed based on the full-condition calibration of standard weights under different cargo temperatures and different lifting heights. The ratio between the difference and the target system gain is determined as the actual weight of the target cargo.

[0012] Optionally, the step of filtering the cargo storage locations based on the relationship between the actual weight and the target threshold to obtain a candidate storage location set includes: If the actual weight is greater than or equal to the target threshold, the target goods are identified as heavy goods, and the first storage location corresponding to the heavy goods is determined. If the actual weight is less than the target threshold, the target goods are identified as light goods, and the second storage location corresponding to the light goods is determined. A set of candidate storage locations is determined based on the first storage location and the second storage location.

[0013] Optionally, before determining the target score of a candidate storage location based on the change in deviation and the change in balance after placing the target goods in each storage location in the candidate storage location set, the method further includes: Determine the first total load of the column where the storage location is located, and determine the total load of all areas; The average column load is determined by the ratio between the sum of the first total loads of each column and the total number of columns. The difference between the first total load and the average column load is determined as the column load deviation before the target goods are placed; The sum of the actual weight and the first total load is determined as the new column load; The sum of the first total load of each column and the sum of the actual weight are determined as the new average column load; The difference between the new column load and the new average column load is determined as the new column load deviation after the target goods are placed; The difference between the new column load deviation and the column load deviation is determined as the deviation change. The ratio between the total load and the number of regions is determined as the average total load; Calculate the variance between the total load and the average total load; The new total load after placing the target goods is determined based on the sum of the total load and the actual weight; The ratio between the new total load and the number of regions is determined as the new average total load; Calculate the new variance between the new total load and the new average total load; The difference between the new variance and the original variance is determined as the change in balance.

[0014] Optionally, determining the target score for each candidate storage location based on the change in deviation and the change in balance after placing the target goods in each location in the candidate storage location set includes: The target score of the candidate storage location is determined by the weighted average of the change in deviation and the change in balance after the target goods are placed in each storage location in the candidate storage location set.

[0015] Optionally, determining the target storage location based on the target score includes: The candidate storage location corresponding to the lowest target score is determined as the target storage location.

[0016] Secondly, this application discloses an intelligent warehouse scheduling device, applied to a scheduling system, comprising: The actual weight determination module is used to, upon receiving an inbound task, acquire the target goods through an automated guided vehicle based on the inbound task, and determine the actual weight of the target goods through the on-board controller of the automated guided vehicle, the current temperature of the goods, and the lifting height. The candidate cargo location set acquisition module is used to acquire the actual weight and target cargo ID uploaded by the automated guided vehicle, and filter the cargo storage location according to the relationship between the actual weight and the target threshold to obtain a candidate cargo location set. The target storage location determination module is used to determine the target score of each candidate storage location based on the deviation change and balance change after placing the target goods in each storage location in the candidate storage location set, and to determine the target storage location based on the target score. The handling module is used to send a handling instruction to the automated guided vehicle based on the target storage location and the target cargo ID, so that the automated guided vehicle can handle the target cargo to the target storage location based on the handling instruction; The outbound module is used to determine the location and weight record information of the target goods based on the outbound task when it receives an outbound task, and send a pickup instruction to the automated guided vehicle so that the automated guided vehicle can retrieve the goods based on the pickup instruction and complete the outbound process.

[0017] Optionally, the device further includes: A pressure sensor mounting module is used to install a pressure sensor in the hydraulic circuit of the forklift mast lifting cylinder or at the fork root of the automated guided vehicle. The unloaded reference value determination module is used to determine the unloaded reference value when the automated guided vehicle is unloaded and stationary in a preset horizontal calibration area.

[0018] Optionally, the actual weight determination module includes: A sensor value determination unit is used to determine the sensor value of the pressure sensor when the automated guided vehicle is loading the target cargo; A difference determination unit is used to determine the difference between the sensor value and the no-load reference value; The target system gain determination unit is used to determine the target system gain in a pre-built system gain table based on the current cargo temperature and lifting height; the system gain table is a table constructed based on full-condition calibration of standard weights under different cargo temperatures and different lifting heights; An actual weight determination unit is used to determine the ratio between the difference and the target system gain as the actual weight of the target cargo.

[0019] In this application, the scheduling system first, upon receiving an inbound task, acquires the target goods using an automated guided vehicle (AGV) based on the inbound task, and determines the actual weight of the target goods using the AGV's onboard controller, current goods temperature, and lifting height. It then acquires the actual weight and target goods ID uploaded by the AGV, and filters the goods storage locations based on the relationship between the actual weight and a target threshold to obtain a candidate storage location set. Based on the deviation and balance changes after placing the target goods in each storage location in the candidate storage location set, it determines the target score of the candidate storage location, and determines the target storage location based on the target score. Based on the target storage location and the target goods ID, it sends a handling instruction to the AGV so that the AGV can move the target goods to the target storage location according to the handling instruction. Upon receiving an outbound task, it determines the location and weight record information of the target goods based on the outbound task, and sends a pickup instruction to the AGV so that the AGV can retrieve the goods according to the pickup instruction and complete the outbound process. As can be seen, this application directly and in real-time senses the actual weight of goods on the AGV forklift and feeds this dynamic data back to the scheduling system. Based on the principles of mechanical stability and structural safety of "overall load balancing," the system makes real-time, optimal warehouse location allocation decisions. Based on real load-bearing data, it can more accurately utilize the racking's load-bearing capacity, increasing storage density under absolute safety conditions. Thus, while maximizing warehouse space utilization, it fundamentally ensures the long-term safety and stability of the storage system. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0021] Figure 1 This is a flowchart of an intelligent warehouse scheduling method disclosed in this application; Figure 2 This is a schematic diagram of the structure of an intelligent warehouse scheduling device disclosed in this application; Figure 3 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] In some current technologies, warehouse location allocation relies on static and inaccurate weight data, leading to a significant discrepancy between the system's calculated rack load status and the actual load. This conceals the risks of localized overloading or overall imbalance, creating potential safety hazards. The lack of real-time perception and optimization of the overall load-bearing distribution of the racks results in goods being stored in a state of mechanical imbalance for extended periods, causing additional stress on the rack structure, floor damage, and accelerated equipment wear. Furthermore, the inability to obtain the true weight of goods in real time leads to excessively large safety margins, wasting valuable vertical storage space and load-bearing capacity. To address these technical problems, this application discloses an intelligent warehouse scheduling method and device capable of making real-time, optimal warehouse location allocation decisions. This maximizes warehouse space utilization while fundamentally ensuring the long-term safety and stability of the storage system.

[0024] See Figure 1 As shown in the figure, an embodiment of the present invention discloses an intelligent warehouse scheduling method, applied to a scheduling system, comprising: Step S11: Upon receiving an inbound task, the target goods are acquired via an automated guided vehicle (AGV) based on the inbound task, and the actual weight of the target goods is determined using the AGV's onboard controller, the current cargo temperature, and the lifting height.

[0025] In this embodiment, pressure sensors are first installed in the hydraulic circuit of the forklift mast lifting cylinder or at the fork root of the automated guided vehicle (AGV); the no-load reference value is determined when the AGV is unloaded and stationary in a preset horizontal calibration zone. High-precision pressure sensors are installed in the hydraulic circuit of the forklift mast lifting cylinder or at the fork root. By measuring the changes in cylinder pressure or structural strain when lifting goods, and combining this with vehicle tilt sensor compensation, the onboard controller calculates the actual weight of the goods (W_actual) in real time.

[0026] Upon receiving an inbound task, the system acquires the target cargo using an automated guided vehicle (AGV) based on the task, determines the sensor value of the pressure sensor when the AGV is loading the target cargo, and determines the difference between the sensor value and the empty reference value. Based on the current cargo temperature and lifting height, the system gain is determined from a pre-built system gain table. This system gain table is constructed using standard weights calibrated under various operating conditions at different cargo temperatures and lifting heights. The ratio between the difference and the target system gain is then determined as the actual weight of the target cargo.

[0027] It should be noted that when the AGV is unloaded, the sensor readings will drift (due to changes in temperature and posture), and the force-to-electrical signal conversion relationship (gain K) will also change due to changes in oil temperature and height. System gain changes: oil viscosity, lifting height, and internal leakage in the hydraulic system cause nonlinearity in the force-to-electrical conversion relationship (gain K). Therefore, this application discloses a very precise multi-level calibration process to ensure weighing accuracy and overcome interference factors such as temperature, uneven ground, and mechanical wear. First, a known horizontal calibration area is defined, and the AGV actively drives into this area whenever it is unloaded or idle. The AGV controller collects the pressure sensor readings at this time and combines them with high-precision tilt sensor data to record the unloaded value under different micro-tilt postures. The most accurate unloaded reference value F_base at the current moment is obtained through a filtering algorithm. At the factory, the AGV is calibrated under full working conditions using standard weights at different oil temperatures (T) and different lifting heights (H) to establish a compensation model of K=f(T,H) (such as lookup table or polynomial fitting). During real-time measurement, the system retrieves the corresponding K value from the model based on the current T (oil temperature sensor) and H (lift encoder) values, eliminating the influence of environmental and operating conditions. When lifting goods, the system looks up or calculates a dynamic system gain (K) from a pre-calibrated model based on the current oil temperature (T) and lifting height (H). The weight of the goods is calculated as (reading during lifting - no-load reference value) / dynamic system gain. This eliminates the influence of environmental and operating conditions. Simultaneously, the system periodically allows the AGV to perform self-checks using standard weights, automatically correcting model parameters to achieve long-term stable high accuracy (within ±0.5%). Specifically, the system sets periodic (e.g., weekly) or triggered (e.g., error exceeding tolerance) calibration tasks. The AGV travels to the maintenance point, automatically lifts a standard weight of known weight, compares the measured data with the theoretical value, and automatically corrects the parameters of the gain model f(T,H), achieving closed-loop self-optimization of the measurement system. This process ensures the long-term reliability and high accuracy (within ±0.5%) of the weight data W_actual. In this way, by using AGV forklifts to measure the actual weight of goods in real time and accurately during the handling process, a single, accurate data source is provided for the scheduling system to make decisions, eliminating information bias. The calibrated, accurate weight data and the self-maintaining load-bearing model provide unprecedented data insights for warehouse operations, supporting further analysis and optimization. Through preventative allocation driven by real weight, overloading and long-term imbalance of shelves are fundamentally prevented, transforming safety hazards from "post-event alarms" to "pre-event prevention."

[0028] Step S12: Obtain the actual weight and target cargo ID uploaded by the automated guided vehicle, and filter the cargo storage locations according to the relationship between the actual weight and the target threshold to obtain a set of candidate cargo locations.

[0029] In this embodiment, a virtual warehouse map is maintained in the memory, which records in real time the current load-bearing data of each storage location (Cell), each column of shelves (Column), and each area (Region). This is the "Dynamic Model of Storage Location - Shelf Load-bearing". The AGV forklift reports the "Goods ID" and "Actual Weight" to the central system via wireless network. If the actual weight is greater than or equal to the target threshold, the target goods are determined as heavy goods, and the first storage location corresponding to the heavy goods is determined; if the actual weight is less than the target threshold, the target goods are determined as light goods, and the second storage location corresponding to the light goods is determined; a candidate storage location set is determined based on the first storage location and the second storage location.

[0030] Specifically, when new goods are put into storage, the system will use an algorithm to automatically calculate the optimal storage location. Its logic is "screen first, then select the best". It is judged according to the measured weight (W_actual) of the goods. A weight threshold W_threshold is set for the goods weight W_actual. If W_actual ≥ W_threshold, it is classified as "heavy goods"; if it is heavy goods, it is only allowed to be placed on the bottom layer or the middle layer (such as layers 1 - ③) to ensure the stability and safety of the shelves. If W_actual < W_threshold, it is classified as "light goods" and can be allocated to all layers (preferably the middle and upper layers). This step screens out the storage locations that do not meet the safety rules, and a candidate storage location set is obtained.

[0031] Step S13: Determine the target score of the candidate storage locations based on the deviation change amount and the balance degree change amount after placing the target goods at each storage location in the candidate storage location set, and determine the target storage location based on the target score.

[0032] In this embodiment, to ensure a more even distribution of load across the entire warehouse and avoid "hot spots" where a particular shelf or area bears excessive weight, the algorithm calculates the impact of placing goods in each candidate storage location on the load balance of the entire shelf column and the overall area. A mathematical formula (objective function) scores each candidate storage location, with lower scores being better. The final selection is made based on the storage location that provides the most even load distribution across the entire warehouse. Therefore, the algorithm first determines the first total load of the column containing the storage location, and then determines the total load of all areas. The ratio of the sum of the first total loads of each column to the total number of columns is determined as the average column load. The difference between the first total load and the average column load is determined as the column load deviation before placing the target goods. The sum of the actual weight and the first total load is determined as the new column load. The sum of the first total loads of each column and the sum of the actual weight is determined as the new average column load. The difference between the new column load and the new average column load is determined as the new column load after placing the target goods. Load deviation; the difference between the new column load deviation and the column load deviation is determined as the deviation change; the ratio between the total load and the number of areas is determined as the average total load; the variance between the total load and the average total load is calculated; the new total load after placing the target goods is determined based on the sum of the total load and the actual weight; the ratio between the new total load and the number of areas is determined as the new average total load; the new variance between the new total load and the new average total load is calculated; the difference between the new variance and the variance is determined as the balance change.

[0033] Next, the weighted average of the deviation change and the balance change after placing the target goods in each location in the candidate location set is determined as the target score for the candidate location. Finally, the candidate location corresponding to the lowest target score is determined as the target location. This forces heavier goods to be preferentially allocated to the lower levels of the racking, lowering the overall center of gravity and improving earthquake and overturning resistance. Storage location allocation aims to minimize load differences between rack rows and balance ground pressure, extending the lifespan of racking and flooring. It significantly extends the lifespan of facilities and equipment: balanced load distribution reduces local stress in the racking structure, uneven floor settlement, and abnormal wear of AGVs, comprehensively extending the lifespan of infrastructure. Based on accurate weight allocation, the load-bearing capacity of each rack level can be safely and fully utilized, increasing vertical storage density.

[0034] Step S14: Send a handling instruction to the automated guided vehicle based on the target storage location and the target cargo ID, so that the automated guided vehicle can move the target cargo to the target storage location based on the handling instruction.

[0035] In this embodiment, the selected storage location C_best is assigned to the goods, and the storage location-shelf load-bearing dynamic model is updated immediately. A handling instruction containing the goods ID and C_best is issued to the AGV for execution. Then, the AGV executes the inbound process. Upon successful execution, the target goods are successfully moved to the target storage location, completing the inbound process.

[0036] Step S15: Upon receiving an outbound task, the location and weight record information of the target goods are determined based on the outbound task, and a pickup instruction is sent to the automated guided vehicle so that the automated guided vehicle can retrieve the goods based on the pickup instruction and complete the outbound process.

[0037] In this embodiment, the outbound process is as follows: The WMS (Warehouse Management System) issues an outbound task. The scheduling system queries the model to find the location of the goods and their recorded historical weight. A pickup instruction is issued to the AGV. After the AGV executes the outbound operation and confirms it, the model is updated to zero for the weight of that location. This eliminates tedious manual inventory checks and data entry, achieving fully automated and high-fidelity synchronization between the physical state of the warehouse and the digital model.

[0038] Furthermore, this application's scheduling system binds strictly to the inventory status of the WMS (Warehouse Management System, which provides goods information (goods ID, theoretical dimensions) and task instructions) through rigorous transaction logic. The model's add, delete, and modify operations correspond precisely to physical storage and retrieval instructions. The system has a low-priority background consistency verification thread that can perform "zero-weight verification" (i.e., lifting the vacant storage location and verifying if the weight is zero) when the AGV occasionally passes through an empty storage location, correcting the very few instances of inconsistencies caused by communication failures. This design allows the model to "grow" synchronously with actual operations from day one without any human intervention, consistently maintaining high fidelity.

[0039] In summary, the scheduling system in this application first, upon receiving an inbound task, acquires the target goods using an automated guided vehicle (AGV) based on the inbound task, and determines the actual weight of the target goods using the AGV's onboard controller, current goods temperature, and lifting height; acquires the actual weight and target goods ID uploaded by the AGV, and filters the goods storage locations based on the relationship between the actual weight and a target threshold to obtain a candidate storage location set; determines the target score of each candidate storage location based on the deviation change and balance change after placing the target goods in each storage location in the candidate storage location set, and determines the target storage location based on the target score; sends a handling instruction to the AGV based on the target storage location and the target goods ID, so that the AGV can move the target goods to the target storage location based on the handling instruction; upon receiving an outbound task, the system determines the location and weight record information of the target goods based on the outbound task, sends a pickup instruction to the AGV, so that the AGV can retrieve the goods based on the pickup instruction and complete the outbound process. As can be seen, this application directly and in real-time senses the actual weight of goods on the AGV forklift and feeds this dynamic data back to the scheduling system. Based on the principles of mechanical stability and structural safety of "overall load balancing," the system makes real-time, optimal warehouse location allocation decisions. Based on real load-bearing data, it can more accurately utilize the racking's load-bearing capacity, increasing storage density under absolute safety conditions. Thus, while maximizing warehouse space utilization, it fundamentally ensures the long-term safety and stability of the storage system.

[0040] See Figure 2 As shown in the figure, an embodiment of the present invention discloses an intelligent warehouse scheduling device, applied to a scheduling system, comprising: The actual weight determination module 11 is used to, upon receiving an inbound task, acquire the target goods through an automated guided vehicle based on the inbound task, and determine the actual weight of the target goods through the on-board controller of the automated guided vehicle, the current temperature of the goods, and the lifting height. The candidate cargo location set acquisition module 12 is used to acquire the actual weight and target cargo ID uploaded by the automated guided vehicle, and filter the cargo storage location according to the relationship between the actual weight and the target threshold to obtain a candidate cargo location set. The target storage location determination module 13 is used to determine the target score of the candidate storage location based on the deviation change and balance change after the target goods are placed in each storage location in the candidate storage location set, and to determine the target storage location based on the target score. The handling module 14 is used to send a handling instruction to the automated guided vehicle based on the target storage location and the target cargo ID, so that the automated guided vehicle can handle the target cargo to the target storage location based on the handling instruction; The outbound module 15 is used to determine the location and weight record information of the target goods based on the outbound task when it receives an outbound task, and send a pickup instruction to the automated guided vehicle so that the automated guided vehicle can retrieve the goods based on the pickup instruction and complete the outbound process.

[0041] In some specific embodiments, the device can also be used to install pressure sensors in the hydraulic circuit of the forklift mast lifting cylinder of the automated guided vehicle or at the fork root; to determine the no-load reference value when the automated guided vehicle is unloaded and stationary in a preset horizontal calibration zone.

[0042] In some specific embodiments, the actual weight determination module 11 can be used to determine the sensor value of the pressure sensor when the automated guided vehicle is loading the target cargo; determine the difference between the sensor value and the empty reference value; determine the target system gain in a pre-built system gain table based on the current cargo temperature and lifting height; the system gain table is a table constructed based on standard weights calibrated under all working conditions at different cargo temperatures and different lifting heights; and determine the ratio between the difference and the target system gain as the actual weight of the target cargo.

[0043] In some specific embodiments, the candidate storage location set acquisition module 12 can be used to determine the target goods as heavy goods and determine the first storage location corresponding to the heavy goods if the actual weight is greater than or equal to the target threshold; if the actual weight is less than the target threshold, determine the target goods as light goods and determine the second storage location corresponding to the light goods; and determine the candidate storage location set based on the first storage location and the second storage location.

[0044] In some specific embodiments, the device can also be used to determine the first total load of the column where the cargo location is located, determine the total load of all areas; determine the ratio between the sum of the first total loads of each column and the total number of columns as the average column load; determine the difference between the first total load and the average column load as the column load deviation before placing the target cargo; determine the sum of the actual weight and the first total load as the new column load; determine the sum of the first total loads of each column and the sum of the actual weight as the new average column load; determine the difference between the new column load and the new average column load as the column load deviation before placing the target cargo. The following steps are defined: 1. Determine the new column load deviation after placing the target cargo; 2. Determine the difference between the new column load deviation and the column load deviation as the deviation change; 3. Determine the ratio between the total load and the number of areas as the average total load; 4. Calculate the variance between the total load and the average total load; 5. Determine the new total load after placing the target cargo based on the sum of the total load and the actual weight; 6. Determine the ratio between the new total load and the number of areas as the new average total load; 7. Calculate the new variance between the new total load and the new average total load; 8. Determine the difference between the new variance and the variance as the balance change.

[0045] In some specific embodiments, the target cargo location determination module 13 can be used to determine the target score of the candidate cargo location by taking the weighted average of the deviation change and the balance change after placing the target goods in each cargo location in the candidate cargo location set.

[0046] In some specific embodiments, the target storage location determination module 13 can be used to determine the candidate storage location corresponding to the smallest target score as the target storage location.

[0047] Furthermore, embodiments of this application also disclose an electronic device, Figure 3 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0048] Figure 3 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the intelligent warehouse scheduling method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0049] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0050] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0051] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the intelligent warehouse scheduling method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include a computer program capable of performing other specific tasks.

[0052] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned intelligent warehouse scheduling method. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0053] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0054] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0055] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0056] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0057] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An intelligent warehouse scheduling method, characterized in that, Applied to scheduling systems, including: Upon receiving an inbound task, the system acquires the target goods using an automated guided vehicle (AGV) based on the inbound task, and determines the actual weight of the target goods using the AGV's onboard controller, the current temperature of the goods, and the lifting height. Obtain the actual weight and target cargo ID uploaded by the automated guided vehicle, and filter the cargo storage locations according to the relationship between the actual weight and the target threshold to obtain a set of candidate cargo locations; The target score of the candidate storage location is determined based on the deviation change and balance change after the target goods are placed in each storage location in the candidate storage location set, and the target storage location is determined based on the target score. Based on the target storage location and the target cargo ID, a handling instruction is sent to the automated guided vehicle (AGV) so that the AGV can move the target cargo to the target storage location based on the handling instruction. Upon receiving an outbound task, the location and weight of the target goods are determined based on the outbound task, and a pickup instruction is sent to the automated guided vehicle (AGV) so that the AGV can retrieve the goods based on the pickup instruction and complete the outbound process.

2. The intelligent warehouse scheduling method according to claim 1, characterized in that, Before determining the actual weight of the target cargo using the onboard controller of the automated guided vehicle, the current cargo temperature, and the lifting height, the method further includes: A pressure sensor is installed in the hydraulic circuit of the forklift mast lifting cylinder or at the fork root of the automated guided vehicle. Determine the unloaded reference value when the automated guided vehicle is unloaded and stationary in a preset horizontal calibration zone.

3. The intelligent warehouse scheduling method according to claim 2, characterized in that, Determining the actual weight of the target cargo using the onboard controller of the automated guided vehicle, the current cargo temperature, and the lifting height includes: Determine the sensor value of the pressure sensor when the automated guided vehicle is loading the target cargo; Determine the difference between the sensor value and the no-load reference value; The target system gain in the pre-built system gain table is determined based on the current cargo temperature and lifting height; the system gain table is constructed based on the full-condition calibration of standard weights under different cargo temperatures and different lifting heights. The ratio between the difference and the target system gain is determined as the actual weight of the target cargo.

4. The intelligent warehouse scheduling method according to claim 1, characterized in that, The step of filtering cargo storage locations based on the relationship between the actual weight and the target threshold to obtain a candidate storage location set includes: If the actual weight is greater than or equal to the target threshold, the target goods are identified as heavy goods, and the first storage location corresponding to the heavy goods is determined. If the actual weight is less than the target threshold, the target goods are identified as light goods, and the second storage location corresponding to the light goods is determined. A set of candidate storage locations is determined based on the first storage location and the second storage location.

5. The intelligent warehouse scheduling method according to claim 1, characterized in that, Before determining the target score of a candidate storage location based on the change in deviation and the change in balance after placing the target goods in each storage location in the candidate storage location set, the method further includes: Determine the first total load of the column where the storage location is located, and determine the total load of all areas; The average column load is determined by the ratio between the sum of the first total loads of each column and the total number of columns. The difference between the first total load and the average column load is determined as the column load deviation before the target goods are placed; The sum of the actual weight and the first total load is determined as the new column load; The sum of the first total load of each column and the sum of the actual weight are determined as the new average column load; The difference between the new column load and the new average column load is determined as the new column load deviation after the target goods are placed; The difference between the new column load deviation and the column load deviation is determined as the deviation change. The ratio between the total load and the number of regions is determined as the average total load; Calculate the variance between the total load and the average total load; The new total load after placing the target goods is determined based on the sum of the total load and the actual weight; The ratio between the new total load and the number of regions is determined as the new average total load; Calculate the new variance between the new total load and the new average total load; The difference between the new variance and the original variance is determined as the change in balance.

6. The intelligent warehouse scheduling method according to claim 5, characterized in that, The determination of the target score for each candidate storage location based on the change in deviation and the change in balance after placing the target goods in each storage location in the candidate storage location set includes: The target score of the candidate storage location is determined by the weighted average of the change in deviation and the change in balance after the target goods are placed in each storage location in the candidate storage location set.

7. The intelligent warehouse scheduling method according to any one of claims 1 to 6, characterized in that, The determination of the target storage location based on the target score includes: The candidate storage location corresponding to the lowest target score is determined as the target storage location.

8. An intelligent warehouse scheduling device, characterized in that, Applied to scheduling systems, including: The actual weight determination module is used to, upon receiving an inbound task, acquire the target goods through an automated guided vehicle based on the inbound task, and determine the actual weight of the target goods through the on-board controller of the automated guided vehicle, the current temperature of the goods, and the lifting height. The candidate cargo location set acquisition module is used to acquire the actual weight and target cargo ID uploaded by the automated guided vehicle, and filter the cargo storage location according to the relationship between the actual weight and the target threshold to obtain a candidate cargo location set. The target storage location determination module is used to determine the target score of each candidate storage location based on the deviation change and balance change after placing the target goods in each storage location in the candidate storage location set, and to determine the target storage location based on the target score. The handling module is used to send a handling instruction to the automated guided vehicle based on the target storage location and the target cargo ID, so that the automated guided vehicle can handle the target cargo to the target storage location based on the handling instruction; The outbound module is used to determine the location and weight record information of the target goods based on the outbound task when it receives an outbound task, and send a pickup instruction to the automated guided vehicle so that the automated guided vehicle can retrieve the goods based on the pickup instruction and complete the outbound process.

9. The intelligent warehouse scheduling device according to claim 8, characterized in that, The device further includes: A pressure sensor mounting module is used to install a pressure sensor in the hydraulic circuit of the forklift mast lifting cylinder or at the fork root of the automated guided vehicle. The unloaded reference value determination module is used to determine the unloaded reference value when the automated guided vehicle is unloaded and stationary in a preset horizontal calibration area.

10. The intelligent warehouse scheduling device according to claim 8, characterized in that, The actual weight determination module includes: A sensor value determination unit is used to determine the sensor value of the pressure sensor when the automated guided vehicle is loading the target cargo; A difference determination unit is used to determine the difference between the sensor value and the no-load reference value; The target system gain determination unit is used to determine the target system gain in a pre-built system gain table based on the current cargo temperature and lifting height; the system gain table is a table constructed based on full-condition calibration of standard weights under different cargo temperatures and different lifting heights; An actual weight determination unit is used to determine the ratio between the difference and the target system gain as the actual weight of the target cargo.

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