An intelligent warehouse management system and method for an agricultural platform

Abstract

This invention relates to the field of logistics management technology, and more particularly to an intelligent warehouse management system and method for an agricultural input platform. The system collects storage information in the warehouse through an information acquisition module; constructs a twin model that maps to the storage information through a model building module, and updates the storage data in the twin model based on inbound and outbound information; generates a target storage plan including several time nodes and target storage quantities based on the inbound and outbound information through a target construction module; periodically calculates the cumulative storage volume of goods in the warehouse based on the twin model; determines whether the storage process meets standards by comparing the cumulative storage volume with the target storage volume, and generates corresponding instructions if standards are not met; and optimizes relevant parameters in the warehouse management system according to the instructions to improve the storage efficiency of goods during the storage process, thereby improving warehouse management efficiency.

Description

Technical Field

[0001] This invention relates to the field of logistics management technology, and in particular to an intelligent warehouse management system and method for an agricultural input platform. Background Technology

[0002] The large-scale development of agricultural production has led to a surge in the volume of agricultural input storage, and traditional management methods can no longer meet the demand for efficient turnover of large quantities of materials. In traditional warehousing operations, the processes of material receiving, issuing, and inventory counting rely heavily on manual operations, which are not only inefficient but also have a high error rate, seriously affecting agricultural production efficiency. Therefore, intelligent technologies can be adopted to improve agricultural input storage management.

[0003] Chinese Patent Application Publication No. CN118552128A discloses a logistics and warehousing management system based on digital twins. This technical solution utilizes a collaborative working module encompassing warehousing equipment, order acquisition, goods allocation, goods tracking, and tracking compensation to determine the compensation system required during goods allocation. By adjusting the goods allocation results through the compensation system, logistics efficiency and warehouse space utilization can be improved. While this solution incorporates digital twin technology for dynamic management of different types of goods, it relies on a compensation system to adjust goods allocation for increased efficiency. However, in actual warehousing management, goods change in real-time during storage. Therefore, optimizing the storage process is crucial for warehousing management. This solution lacks a method for optimizing and adjusting the storage process of goods in the warehouse, which could negatively impact warehousing management efficiency. Summary of the Invention

[0004] To address this issue, the present invention provides an intelligent warehouse management system for an agricultural input platform, which overcomes the problem of low warehouse management efficiency caused by the failure to optimize and adjust the corresponding parameters in the warehouse management system based on the judgment of the goods storage process in the existing technology.

[0005] To achieve the above objectives, the present invention provides an intelligent warehouse management system for an agricultural input platform, comprising:

[0006] The goods receiving module is used to send receiving information;

[0007] The goods outbound module is used to send outbound information;

[0008] The cargo transportation module includes several AGVs used to transport goods within the warehouse;

[0009] The information collection module is used to periodically collect storage information of each shelf in the warehouse;

[0010] The model building module is used to obtain the storage information and build a twin model, and to obtain the inbound information and the outbound information and periodically update the corresponding storage data in the twin model, wherein the storage data and the corresponding storage information have a mapping relationship;

[0011] A target construction module, which is connected to the goods inbound module and the goods outbound module respectively, is used to determine a target storage plan based on the inbound information and the outbound information. The target storage plan includes several time nodes and several target storage quantities corresponding to each time node.

[0012] A statistics module, which is connected to the model building module, is used to periodically calculate the cumulative storage volume of goods in the warehouse based on the stored data;

[0013] An analysis module, which is connected to the target construction module and the statistics module respectively, is used to determine whether the cargo storage process meets the standard based on the comparison between the cumulative storage amount and the target storage amount, and to generate instructions if the standard is not met.

[0014] An adjustment module, which is connected to the analysis module, the cargo transportation module, the information collection module, and the target construction module respectively, is used to determine the detection frequency of the information collection module, the transportation speed of each AGV, the target storage plan, or each time node based on the instructions.

[0015] Furthermore, the analysis module is also used to determine whether the goods storage process meets the standard based on the comparison result of the storage ratio and the preset storage ratio, or to determine whether the goods storage process meets the standard by combining the comparison result of the storage ratio at the current time node and the storage ratio at the previous time node.

[0016] The analysis module is also used to determine the reason for non-compliance with standards based on the difference between the preset storage ratio and the storage ratio when it is determined that the goods storage process does not meet the standards.

[0017] The storage ratio is the ratio of the cumulative storage amount to the corresponding target storage amount at the same time point.

[0018] Furthermore, the analysis module is also used to determine whether to increase the detection frequency based on the comparison result of the storage ratio and the historical storage ratio;

[0019] Wherein, the historical storage ratio is the storage ratio corresponding to the current time node, and the detection frequency is the frequency at which the information acquisition module collects the stored data.

[0020] Furthermore, the analysis module is also used to generate corresponding instructions based on the comparison results of the periodic stacking difference and the preset periodic stacking difference to determine the adjustment of the detection frequency;

[0021] The adjustment module is also used to control the information acquisition module to increase the detection frequency based on the instruction, and the increase in the detection frequency is positively correlated with the period stacking difference;

[0022] Wherein, the periodic stacking difference is the difference between the storage ratio and the historical storage ratio.

[0023] Furthermore, the analysis module is also used to determine the reasons why the goods storage process does not meet the standards based on the comparison results between the storage ratio difference and the preset storage ratio difference;

[0024] The analysis module is also used to determine, based on the reasons, instructions to adjust the transport speed of each AGV, update the target storage plan, or redetermine the corresponding processing;

[0025] The adjustment module is also used to adjust the transport speed of each AGV based on the instruction, or to update the target storage plan;

[0026] Wherein, the storage ratio difference is the difference between the preset storage ratio and the storage ratio.

[0027] Furthermore, the analysis module is also used to generate corresponding instructions based on the comparison results between the storage slope and the preset storage slope to adjust the transportation speed of each AGV;

[0028] The adjustment module is also used to control the cargo transportation module to increase the transportation speed of each AGV based on the instruction, and the increase in transportation speed is positively correlated with the storage slope;

[0029] The analysis module is further configured to construct a time-cumulative storage curve based on several time nodes and the cumulative storage amount corresponding to each time node, wherein the storage slope is the slope corresponding to the current time node on the time-cumulative storage curve.

[0030] Furthermore, the analysis module is also used to generate corresponding instructions to increase the number of AGVs when the storage ratio difference is less than or equal to the preset storage ratio difference after the adjustment of the transportation speed of each AGV is completed.

[0031] The adjustment module is also used to issue a notification to increase the number of AGVs scheduled based on the instruction, and the increased number of scheduling is positively correlated with the difference in storage capacity;

[0032] Wherein, the storage difference is the difference between the target storage amount corresponding to the last time node within the preset detection period and the cumulative storage amount corresponding to the current time node.

[0033] Furthermore, the analysis module is also used to determine whether to update the target storage plan based on the comparison result of the fitting overlap degree and the preset fitting overlap degree.

[0034] The target construction module is also used to construct a time-heap size curve based on the target storage plan;

[0035] The target construction module is also used to generate an outbound plan based on the outbound information, and to fit the target storage plan and the outbound plan to obtain a fitted plan and to construct an update time-stacking curve based on the fitted plan;

[0036] The analysis module is further used to calculate the fitting overlap based on the time-stacking volume curve and the update time-stacking volume curve.

[0037] Furthermore, the analysis module is also used to generate corresponding instructions based on the comparison results of stacking overlap degree and preset stacking overlap degree to determine to re-correct each of the time nodes, or to issue a notification to optimize the twin model.

[0038] The adjustment module is also used to control the target construction module to determine and correct each of the time nodes based on the instructions, or to issue a notification to optimize the twin model;

[0039] The analysis module is also used to calculate the stacking overlap based on the time-stack quantity curve and the time-cumulative storage curve.

[0040] This invention also provides an intelligent warehousing management method for an agricultural input platform, comprising:

[0041] Send inbound information through the goods inbound module;

[0042] Send outbound information through the goods outbound module;

[0043] Goods are transported in the warehouse using a cargo transport module that includes several AGVs;

[0044] The storage information corresponding to each shelf in the warehouse is periodically collected by the information collection module;

[0045] The storage information is obtained through the model building module and a twin model is constructed. The storage data in the twin model is periodically updated based on the inbound information and the outbound information, wherein there is a mapping relationship between the storage data and the corresponding storage information.

[0046] The target construction module, which is connected to the goods inbound module and the goods outbound module respectively, determines the corresponding target storage plan based on the inbound information and the outbound information. The target storage plan includes several time nodes and several target storage quantities corresponding to each time node.

[0047] The statistical module, connected to the information collection module, periodically calculates the cumulative storage volume of goods in the warehouse based on the stored data.

[0048] An analysis module, which is connected to the target construction module and the statistics module respectively, determines whether the cargo storage process meets the standard based on the cumulative storage amount and the target storage amount, and generates an instruction when it is determined that the standard is not met.

[0049] The adjustment module, which is connected to the analysis module, the cargo transportation module, the information collection module, and the target construction module respectively, determines the detection frequency of the information collection module, the transportation speed of each AGV, the target storage plan, or the time nodes based on the instructions.

[0050] Compared with existing technologies, the intelligent warehouse management system for an agricultural input platform of the present invention has the following advantages: the system transports goods in the warehouse through a goods transportation module including several AGVs; it periodically collects storage information in the warehouse through an information collection module, and constructs a twin model based on the storage information through a model building module. The storage data in the twin model has a mapping relationship with the storage information, and the storage data in the twin model is periodically updated according to the inbound information in the goods inbound module and the outbound information in the goods outbound module; the target building module generates target storage based on the inbound and outbound information, including several time nodes and several target storage quantities corresponding to each time node. The system comprises a storage plan and a statistics module. Based on the twin model's storage data, the statistics module periodically calculates the cumulative storage volume of goods in the warehouse. The analysis module compares the cumulative storage volume with the target storage volume to determine whether the storage process meets the standards. If the storage process does not meet the standards, the analysis module generates corresponding instructions. The adjustment module determines the detection frequency of the information collection module, the transportation speed of the AGV, the target storage plan, or the time node based on the instructions. By optimizing the parameters in the corresponding modules, the system achieves self-adjustment, thereby improving the storage and transportation efficiency of agricultural goods, thus improving warehouse management efficiency and reducing the company's labor and operating costs.

[0051] Furthermore, the present invention further determines whether the goods storage process at the current time node conforms to the standard based on the comparison result between the storage ratio and the preset storage ratio. When it is determined that the storage process does not conform to the standard, the reason for non-compliance can be determined based on the difference between the preset storage ratio and the storage ratio, thereby enabling targeted correction processing.

[0052] Furthermore, in order to reduce misjudgments when determining the storage process of goods, the present invention can further compare and determine the storage ratio at the current time point with the historical storage ratio at the previous time point to improve the accuracy of the determination.

[0053] Furthermore, when re-evaluating, the present invention can determine to increase the detection frequency based on the comparison between the storage ratio and the historical storage ratio, thereby increasing the number of calculations for the cumulative storage amount and the target storage amount within the preset detection period, making the storage ratio calculation result more accurate and providing the accuracy of the comparison result.

[0054] Furthermore, when the present invention determines that the storage process of goods does not meet the standards, it can also determine the corresponding cause based on the comparison result of the storage ratio difference and the preset storage ratio difference, and determine the corresponding correction method based on the cause. Then, the adjustment module is used to adjust other modules, thereby improving the storage and transportation efficiency of agricultural goods in the storage process.

[0055] Furthermore, when determining the corresponding processing, the present invention can also determine, based on the comparison between the storage slope and the preset storage slope, that the goods can be stacked on the corresponding shelves in a timely manner by increasing the transport speed of the AGV. In this way, the difference between the cumulative storage amount and the target storage amount can be reduced within the preset detection cycle, thereby improving the storage efficiency of the goods in the storage process and thus improving the efficiency of warehouse management.

[0056] Furthermore, after completing the AGV speed adjustment, the present invention further determines the number of AGVs that need to be increased based on the comparison result between the storage quantity difference and the preset storage quantity difference. That is, more AGVs are started on the basis of the original number of AGVs used. This can reduce the difference between the cumulative storage quantity and the target storage quantity at the last time node within the preset detection cycle, thereby improving the transportation efficiency of goods in the storage process and thus improving the efficiency of warehouse management.

[0057] Furthermore, when determining the reasons why the goods storage process does not meet the standards, the present invention further determines the reasons for non-compliance based on the comparison between the fitting overlap degree and the preset fitting overlap degree. When it is determined that the goods leaving the warehouse does not affect the judgment based on the storage ratio, it can be determined that the problem is a storage problem in the process of collecting storage information. When it is determined that the goods leaving the warehouse affects the judgment based on the storage ratio, it can be determined that the target storage plan needs to be updated. In this way, when the reasons cannot be accurately determined, the reasons and corresponding correction methods can be more targeted based on the fitting overlap degree, thereby taking into account the impact of the goods leaving the warehouse and improving the accuracy of determining the reasons.

[0058] Furthermore, the present invention also determines the corresponding processing method based on the comparison between the stacking overlap degree and the preset stacking overlap degree. When it is determined that the cumulative storage amount differs significantly from the corresponding target storage amount, it can be determined that there is a problem with the twin model and issue a notification to optimize the model. Alternatively, when it is determined that the cumulative storage amount differs significantly from the corresponding target storage amount, it can be determined that there is a problem with the time nodes in the target storage plan and the time nodes in the target storage plan need to be postponed accordingly. In this way, the impact of mismatch between the time-stack amount curve and the time-cumulative storage curve and the overlap degree can be reduced, thereby reducing the difference between the cumulative storage amount and the target storage amount, and thus improving the storage efficiency of goods during the storage process.

[0059] Furthermore, this invention generates a twin model based on digital twin technology that maps to warehouse storage information. Based on the model, the warehousing process can be quickly determined without requiring a large number of personnel to enter the warehouse for inspection. This allows for accurate determination of the warehouse storage situation and improves inspection efficiency. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of the modules of an intelligent warehouse management system for an agricultural input platform according to this embodiment;

[0061] Figure 2 This is a flowchart illustrating an intelligent warehousing management method for an agricultural input platform according to this embodiment;

[0062] Figure 3 This is a logic diagram for determining whether the goods storage process meets the standard based on the storage ratio in this embodiment;

[0063] Figure 4 This embodiment presents a logic diagram for determining the reasons why the goods storage process does not meet the standards and for making corrections based on the storage ratio difference.

[0064] Figure 5 This is a logic diagram for re-determining the reasons why the cargo storage process does not meet the standards based on the stacking overlap in this embodiment, and for making corrections. Detailed Implementation

[0065] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0066] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0067] It should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0068] Please see Figure 1The diagram shown is a schematic of the modules of an intelligent warehouse management system for an agricultural input platform in this embodiment. The system includes: a goods inbound module, a goods outbound module, a goods transportation module, an information collection module, a model building module, a target construction module, a statistics module, an analysis module, and an adjustment module. Specifically, the goods inbound module includes a first signal transmitter for sending inbound information; the goods outbound module includes a second signal transmitter for sending outbound information; the goods transportation module includes several AGVs for transporting goods in the warehouse; the information collection module collects storage information from each shelf in the warehouse; the model building module acquires the storage information and constructs a twin model, and acquires the inbound and outbound information and periodically updates the corresponding storage data in the twin model, wherein the storage data and the corresponding storage information have a mapping relationship; the target construction module is connected to both the goods inbound and goods outbound modules, and determines a target storage plan based on the inbound and outbound information, wherein the target storage plan includes... The system comprises several time points and several target storage quantities corresponding to each time point; a statistics module, connected to the model building module, used to periodically calculate the cumulative storage quantity of goods in the warehouse based on the storage data; an analysis module, connected to the target building module and the statistics module respectively, used to determine whether the goods storage process meets the standard based on the comparison between the cumulative storage quantity and the target storage quantity, and to generate instructions if the goods storage process does not meet the standard; and an adjustment module, connected to the analysis module, the goods transportation module, the information collection module, and the target building module respectively, used to determine the detection frequency of the information collection module, the transportation speed of several AGVs, the target storage plan, or each time point based on the instructions.

[0069] Specifically, in this embodiment, the inbound information includes the quantity, time, and category of goods entering the warehouse, and the outbound information includes the quantity, time, and category of goods leaving the warehouse. For example, the categories of goods on the agricultural input platform include seeds, pesticides, etc. The inbound information is sent via a first signal transmitter, and the outbound information is sent via a second signal transmitter. Several AGVs receive the inbound and outbound information and then either stack the goods to be stored on the corresponding shelves or remove the goods to be outbound from the corresponding shelves and send them out of the warehouse. In this embodiment, shelf storage is selected, but it is understood that stacking storage and loose storage can also be selected. The storage information changes in real time with the inbound and outbound information, and includes the quantity of stored goods. Information collection... The module periodically collects information data on each shelf in the warehouse, along with the corresponding goods on the shelves and their empty locations, using an information collector (which may include a camera and a monitor). This data is then stored. A model building module connects to the goods inbound module, goods outbound module, and information collection module. Based on the stored data, a digital twin model is constructed using digital twin technology. The stored data and information are mapped to each other, accurately reflecting the quantity of goods stored on each shelf. Furthermore, when inbound or outbound information changes, the corresponding stored information also changes; therefore, the stored data needs to be updated. The model building module can then directly adjust the data based on the inbound information. The storage data in the twin model is periodically updated with inbound and outbound information. After the twin model is built, employees can understand the storage status in the warehouse in real time without entering the warehouse, thus providing timely and reliable reference for subsequent goods transportation and inbound / outbound operations. The target construction module generates a target storage plan based on inbound and outbound information. The preset detection cycle includes several time nodes. The target storage plan here is to determine the required storage quantity of goods at each time node in the time dimension, which is recorded as the target storage quantity. The target storage quantity is the amount of goods stored in the warehouse after each item to be stored in the inbound information is placed on the corresponding shelf. The quantity of goods to be stored in the warehouse is adjusted by the target storage quantity. The module can also obtain the expected storage volume of goods at each expected time point according to the target storage plan, and then fit and draw a time-stacking volume curve based on this; the statistics module periodically collects storage data that is mapped to the storage situation of goods in the warehouse, and then determines the actual cumulative storage volume in the warehouse at each time point; the analysis module obtains the target storage volume and the cumulative storage volume, and then determines whether the storage process of goods meets the standard based on the comparison between the target storage volume and the cumulative storage volume. At this time, the target storage volume and the cumulative storage volume are obtained at the same time point. When it is determined that the storage process does not meet the standard, the corresponding reasons can be identified, and then the analysis module generates corresponding processing instructions based on the reasons.Then, after receiving instructions, the adjustment module optimizes the parameters in the corresponding module, thereby improving the storage and transportation efficiency of goods during the storage process, thus improving warehouse management efficiency and reducing the company's labor and operating costs.

[0070] Please see Figure 2 The diagram shown is a flowchart illustrating an intelligent warehousing management method for an agricultural input platform in this embodiment. The process includes at least the following steps:

[0071] S1: Send inbound information through the goods inbound module;

[0072] S2: Send outbound information through the goods outbound module;

[0073] S3: Transporting goods in the warehouse using a cargo transport module that includes several AGVs;

[0074] S4: Periodically collect storage information corresponding to each shelf in the warehouse through the information collection module;

[0075] S5: Obtain the storage information through the model building module and build a twin model, and periodically update the storage data in the twin model based on the inbound information and the outbound information, wherein there is a mapping relationship between the storage data and the corresponding storage information;

[0076] S6: The target construction module, which is connected to the goods inbound module and the goods outbound module respectively, determines the corresponding target storage plan based on the inbound information and the outbound information. The target storage plan includes several time nodes and several target storage quantities corresponding to each time node.

[0077] S7: The statistical module connected to the information collection module periodically calculates the cumulative storage volume of goods in the warehouse based on the stored data;

[0078] S8: An analysis module, which is connected to the target construction module and the statistics module respectively, determines whether the cargo storage process meets the standard based on the cumulative storage amount and the target storage amount, and generates an instruction if it does not meet the standard.

[0079] S9: The adjustment module, which is connected to the analysis module, the cargo transportation module, the information collection module, and the target construction module respectively, determines the detection frequency of the information collection module, the transportation speed of each AGV, the target storage plan, or each time node based on the instructions.

[0080] Please see Figure 3As shown, this is a logic diagram for determining whether the goods storage process meets the standard based on the storage ratio in this embodiment. The analysis module is also used to determine whether the goods storage process meets the standard based on the comparison result of the storage ratio and the pre-stored preset storage ratio, or, in combination with the comparison result of the storage ratio at the current time node and the storage ratio at the previous time node, to determine whether the goods storage process meets the standard; the analysis module is also used to determine the reason for non-compliance based on the difference between the preset storage ratio and the storage ratio when it is determined that the goods storage process does not meet the standard; wherein, the storage ratio is the ratio of the cumulative storage amount to the corresponding target storage amount at the same time node.

[0081] Specifically, in this embodiment, by statistically analyzing the cumulative storage amount and target storage amount at each time point, the storage ratio R is calculated. A preset storage ratio R0 is set and compared with R to determine the relationship between the cumulative storage amount and the target storage amount, thereby determining whether the goods storage process meets the standard. To more accurately determine the goods storage process, R0 can be further divided into a first preset storage ratio R1 and a second preset storage ratio R2, where R1 corresponds to the pass line and R2 corresponds to the fail line. R1 = 0.97 and R2 = 0.94 are set. The comparison process between R and R1 and R2 is as follows:

[0082] If R is greater than or equal to R1, then R is considered acceptable. This indicates that the cumulative storage volume at the current time point is very close to the target storage volume in the target storage plan, meaning the warehouse storage requirements have met the expected standards. Therefore, the storage and transportation process is deemed compliant. If R is less than R1 but greater than or equal to R2, the storage ratio R at the current time point and the storage ratio R* at the previous time point can be obtained and compared further to determine the storage situation, thus improving the accuracy of the determination. R* is recorded as the historical storage ratio. If R is less than R2, then R is considered unacceptable. This indicates that the cumulative storage volume at the current time point is far less than the target storage volume, meaning the warehouse storage requirements have not met the expected standards. Therefore, the storage process is deemed non-compliant. In this case, the difference between the second preset storage ratio R2 and the storage ratio R is calculated and recorded as the storage ratio difference Q. Based on Q, the reason for non-compliance is determined. It is understood that R1 and R2 are not specifically limited in the embodiments of the present invention, and the above values ​​are not limited thereto. Those skilled in the art can adjust R1 and R2 according to actual needs.

[0083] Furthermore, the analysis module is also used to determine whether to increase the detection frequency based on the comparison result of the storage ratio and the historical storage ratio; wherein, the historical storage ratio is the storage ratio corresponding to the current time node, and the detection frequency is the frequency at which the information acquisition module collects the stored data.

[0084] Specifically, in this embodiment, the comparison process between the storage ratio R and the historical storage ratio R* is as follows: if R is less than R*, it means that R corresponding to the previous time node or the first detection time node is larger than R at the next time node. Therefore, it can be determined that R is decreasing over time, and the cumulative storage amount is gradually moving away from the target storage amount. In the case of ensuring the maximum storage utilization rate of the warehouse, it can be determined that the storage process of the goods does not meet the standard. At this time, it is necessary to determine the reason based on the storage ratio difference Q.

[0085] If R is greater than or equal to R*, it means that R at the current time point is not less than R before that time point. It can be determined that R is increasing or remaining flat as time progresses. The cumulative storage amount is gradually approaching the target storage amount over time, and the rate of increase of goods is also accelerating. At this time, it is necessary to increase the detection frequency and thus increase the number of detections within the cycle, so as to obtain storage information more accurately.

[0086] Furthermore, the analysis module is also used to generate corresponding instructions based on the comparison results of the periodic stacking difference and the pre-stored preset periodic stacking difference to determine the adjustment of the detection frequency; the adjustment module is also used to control the information acquisition module to increase the detection frequency based on the instructions, and the increase in the detection frequency is positively correlated with the periodic stacking difference; wherein, the periodic stacking difference is the difference between the storage ratio and the historical storage ratio.

[0087] Specifically, in this embodiment, the periodic stacking difference Y is the difference between the storage ratio R and the historical storage ratio R*. Y represents the magnitude of the increase in the storage ratio R over time. The larger Y is, the greater R is at a later time point compared to the previous time point, indicating a faster change in the accumulated storage. Therefore, it is necessary to appropriately increase the detection frequency to adapt to the changes in the accumulated storage. To refine the increase in the detection frequency during the adjustment process, Y0 can be divided into a first preset periodic stacking difference Y1 and a second preset periodic stacking difference Y2, with Y1=0.01 and Y2=0.015. The comparison process between Y and Y1 and Y2 is as follows:

[0088] If Y is less than or equal to Y1, the analysis module generates an instruction for a first frequency adjustment coefficient, and the adjustment module, based on the instruction, controls the information acquisition module to increase the original detection frequency by 20%. If Y is greater than Y1 and less than or equal to Y2, the analysis module generates an instruction for a second frequency adjustment coefficient, and the adjustment module, based on the instruction, controls the information acquisition module to increase the original detection frequency by 25%. If Y is greater than Y2, the analysis module generates an instruction for a third frequency adjustment coefficient, and the adjustment module, based on the instruction, controls the information acquisition module to increase the original detection frequency by 30%. It should be noted that the increase in detection frequency can also be set to other values ​​that meet the requirements. For example, if Y is greater than Y2, it can be increased by 35% on the original basis. It is understood that Y1 and Y2 are not specifically limited in this embodiment of the invention, and the above values ​​are not limited to these. Those skilled in the art can adjust Y1 and Y2 according to the actual generated target storage plan.

[0089] At this point, as the detection frequency increases, the number of detection time points also increases. The target storage amount corresponding to the increased time point can be determined based on the time-stacking quantity curve. Then, the ratio between the cumulative storage amount corresponding to the detection time point and the target storage amount is calculated to obtain a new storage ratio, which is then used for comparison and judgment again.

[0090] Please see Figure 4 As shown, this is a logic diagram for determining the reasons why the cargo storage process does not meet the standards and for correction based on the storage ratio difference in this embodiment. The analysis module is also used to determine the reasons why the cargo storage process does not meet the standards based on the comparison result between the storage ratio difference and the preset storage ratio difference; the analysis module is also used to determine, based on the reasons, instructions to adjust the transportation speed of each AGV, update the target storage plan, or re-determine the corresponding processing; the adjustment module is also used to adjust the transportation speed of each AGV or update the target storage plan based on the instructions; wherein, the storage ratio difference is the difference between the preset storage ratio and the storage ratio.

[0091] Specifically, in this embodiment, the storage ratio difference Q is the difference between the second preset storage ratio R2 and the storage ratio R. Q can reflect the amount by which the cumulative storage amount is less than the target storage amount at the same time point. The larger Q is, the greater the difference between the two. A preset storage ratio difference Q0 can be set and compared with Q to determine the reason for the non-compliance of the goods storage process, and then the corresponding handling method can be determined according to the reason. In order to determine the reason more accurately, Q0 can be divided into the first preset storage ratio difference Q1 and the second preset storage ratio difference Q2, and set Q1=0.1 and Q2=0.15. The comparison process based on Q with Q1 and Q2 is as follows:

[0092] If Q is less than or equal to Q1, it indicates that the storage ratio R is relatively close to the preset storage ratio R0. For example, if the target storage quantity at a certain time point is 1000 units, and the minimum cumulative storage quantity is determined to be 900 units, then the cumulative storage quantity is relatively close to the target storage quantity. In this case, the reason why the storage process does not meet the standard can be determined to be due to a problem with the AGV's transport speed. When creating the target storage plan based on the inbound and outbound information, the goods have already been received, but the AGV has not placed the goods on the shelves according to the predetermined time. This results in the cumulative storage quantity based on the storage data statistics not corresponding to the target storage quantity set in the target storage plan in a timely manner. Therefore, it is necessary to adjust the AGV's transport speed to optimize the goods storage process. If Q is greater than Q1 and less than or equal to Q2, it indicates that the difference between R and R0 is moderate, meaning the cumulative storage quantity is moderately close to the target storage quantity. In this case, the reason why the storage process does not meet the standard can be determined to be due to a difference between the warehouse goods storage and the target storage plan after the goods are outbound. It is necessary to determine whether to update the target storage plan based on the outbound information. If Q is greater than Q2, it indicates that R differs significantly from R0, meaning the cumulative storage amount differs significantly from the target storage amount. In this case, a new determination is needed to identify the cause, and a corresponding processing method should be generated based on the newly determined cause. It is understood that Q1 and Q2 are not specifically limited in this embodiment of the invention, and the values ​​mentioned above are not limited to these. Those skilled in the art can adjust Q1 and Q2 according to actual needs.

[0093] Furthermore, the analysis module is also used to generate corresponding instructions based on the comparison result of the storage slope and the preset storage slope to adjust the transportation speed of each AGV; the adjustment module is also used to control the cargo transportation module to increase the transportation speed of each AGV based on the instructions, and the increase in transportation speed is positively correlated with the storage slope; wherein, the analysis module is also used to construct a time-cumulative storage curve based on several time nodes and the cumulative storage amount corresponding to each time node, and the storage slope is the slope corresponding to the current time node on the time-cumulative storage curve.

[0094] Specifically, in this embodiment, the storage slope P is the slope corresponding to the time-cumulative storage curve at the current time point. P represents the instantaneous inbound rate of goods at that moment, i.e., the change in storage volume per unit time. The larger P is, the more goods are inbound. To accommodate the increased quantity of goods, the AGV's transport speed can be appropriately increased so that the target storage volume matches the cumulative storage volume. A preset storage slope P0 can be set and compared with P to determine the increase in the AGV's transport speed, thereby optimizing the goods storage process. To more accurately adjust the AGV's transport speed, P0 can be divided into a first preset storage slope P1 and a second preset storage slope P2, with P1=0.75 and P2=1.15. The comparison process between P and P1 and P2 is as follows:

[0095] If P is less than or equal to P1, the analysis module generates an instruction for the first speed adjustment coefficient. The adjustment module then controls the cargo transport module to increase the AGV's transport speed to 1.25 times the initial value. If P is greater than P1 and less than or equal to P2, the analysis module generates an instruction for the second speed adjustment coefficient. The adjustment module then controls the cargo transport module to increase the AGV's transport speed to 1.45 times the initial value. If P is greater than P2, the analysis module generates an instruction for the third speed adjustment coefficient. The adjustment module then controls the cargo transport module to increase the AGV's transport speed to 1.65 times the initial value. It should be noted that the AGV's transport speed increase factor can also be set to other suitable values ​​to solve the transport problem. For example, when P is greater than P2, the transport speed can be increased to 1.7 times the initial value, as long as the AGV speed adjustment threshold is met. It is understood that P1 and P2 are not specifically limited in the embodiments of the present invention, and the above values ​​are not limited thereto. Those skilled in the art can adjust P1 and P2 according to the actual generated target storage plan.

[0096] Furthermore, the analysis module is also used to generate a corresponding instruction to increase the number of AGVs when the storage ratio difference is less than or equal to the preset storage ratio difference after the transportation speed of each AGV has been increased; the adjustment module is also used to issue a notification to increase the number of AGVs based on the instruction, and the increased number of scheduling is positively correlated with the storage ratio difference; wherein, the storage ratio difference is the difference between the target storage amount corresponding to the last time node within the preset detection period and the cumulative storage amount corresponding to the current time node.

[0097] Specifically, in this embodiment, after adjusting the transport speed of the corresponding AGV and performing a re-comparison, if a transport problem still exists, it is determined that the number of AGV terminals participating in transport within the preset operating cycle is insufficient, and more AGVs need to be started. The adjustment module then issues a notification to increase the number of AGVs participating in transport within the preset operating cycle. The storage difference N is the difference between the target storage amount corresponding to the last time node and the cumulative storage amount corresponding to the current time node. Through N, the amount of goods that are insufficiently stored can be determined. Furthermore, a preset storage difference N0 can be set and compared with N to accurately determine the amount that needs to be increased. The number of AGVs scheduled is increased to match the target storage volume with the cumulative storage volume, thereby optimizing the goods storage process. To more accurately determine the number of scheduled AGVs, N0 can be divided into a first preset storage volume difference N1 and a second preset storage volume difference N2. Based on the target storage plan, the target storage volume at the last time node can be set to 1000 units. Assuming one AGV can transport 10 goods simultaneously, N1 = 20 units and N2 = 30 units. Those skilled in the art can adjust the target storage volume according to actual needs. The comparison process between N and N1 and N2 is as follows:

[0098] If N is less than or equal to N1, the analysis module generates a first quantity adjustment coefficient instruction. The adjustment module then issues a notification to increase the number of AGVs transported by two based on the original quantity. Upon receiving the notification, staff start the corresponding AGVs. If N is greater than N1 and less than or equal to N2, the analysis module generates a second quantity adjustment coefficient instruction. The adjustment module then issues a notification to increase the number of AGVs transported by three based on the original quantity. Upon receiving the notification, staff start the corresponding AGVs. If N is greater than N2, the specific value of N determines the third quantity adjustment coefficient instruction generated by the analysis module. The adjustment module then issues a notification to increase the quantity, and upon receiving the notification, staff start the corresponding AGVs. In this case, N is not infinitely large. It is understood that N1 and N2 are not specifically limited in this embodiment of the invention, and the above values ​​are not limited to these. Those skilled in the art can adjust N1 and N2 according to the actual generated target storage plan.

[0099] Furthermore, the analysis module is also used to determine whether to update the target storage plan based on the comparison result of the fitting overlap degree and the preset fitting overlap degree; the target construction module is also used to construct a time-stacking volume curve based on the target storage plan; the target construction module is also used to generate an outbound plan based on the outbound information, and to perform fitting according to the target storage plan and the outbound plan to obtain a fitted plan and construct an updated time-stacking volume curve based on the fitted plan; wherein, the analysis module is also used to calculate the fitting overlap degree according to the time-stacking volume curve and the updated time-stacking volume curve.

[0100] Specifically, in this embodiment, the target construction module can construct a time-stacking volume curve based on the target storage plan. This curve uses several time nodes and several target storage volumes corresponding to each time node as coordinate axes. The target construction module is also used to generate an outbound plan based on outbound information. Then, the target plan and the outbound plan are fitted to obtain a corresponding fitted plan. This fitted plan considers the impact of goods outbound on the original target storage plan. Then, an updated time-stacking volume curve reflecting the new target storage volume at each time node is constructed based on the fitted plan. The two are then placed in the same coordinate system, and the fitting overlap between them is calculated. Here, the fitting overlap indicates the degree of overlap between the two curves. A preset fitting overlap G0 = 0.9 is set. 0.9 can represent the fitting degree of the time-stacking volume curve and the updated time-stacking volume curve. The matching degree of the two curves is quantified by comparing G and G0. The specific process of comparing the fitting overlap G and G0 is as follows:

[0101] If G is less than G0, the matching degree between the time-stacking quantity curve and the updated time-stacking quantity curve is relatively low, indicating that the outbound plan for constructing the updated time-stacking quantity curve has not affected the judgment process. Therefore, the reason why the goods storage process does not meet the standard can be determined to be that the storage information collection process is unqualified, and the storage information collection process needs to be adjusted. If G is greater than or equal to G0, the matching degree between the time-stacking quantity curve and the updated time-stacking quantity curve is relatively high, indicating that the outbound plan has affected the judgment process. At this time, the target storage plan needs to be updated, that is, the updated time-stacking quantity curve is used, and then the target storage quantity at the current time node is determined based on the curve, thereby recalculating the new storage ratio, and re-judging whether the goods storage process meets the standard based on the new storage ratio. It is understood that G0 is not specifically limited in the embodiments of the present invention. In optional embodiments, a new G0 = 0.95 can be set to further improve the reliability of the judgment result. It is sufficient to determine whether to update the target storage plan by comparing G and G0. The specific comparison process is as described above.

[0102] Please see Figure 5 As shown, this is a logic diagram for re-determining the reasons for non-compliance of the cargo storage process with standards based on the stacking overlap degree in this embodiment, and for correction. The analysis module is also used to generate corresponding instructions based on the comparison results of the stacking overlap degree and the preset stacking overlap degree to determine the need to correct each of the time nodes, or to issue a notification to optimize the twin model; the adjustment module is also used to control the target construction module to determine the need to correct each of the time nodes, or to issue a notification to optimize the twin model, based on the instructions; wherein, the analysis module is also used to calculate the stacking overlap degree based on the time-stack quantity curve and the time-cumulative storage curve.

[0103] Specifically, in this embodiment, when the information acquisition module periodically acquires and stores information, each acquisition time node corresponds to each target time node in the target storage plan. The time-stacking volume curve here is constructed based on the initial target storage plan. The stacking overlap here is obtained only by comparing the image states of the two curves, representing the degree of similarity of the overall shapes of the two curves. A preset stacking overlap E0 = 80% is set. An 80% similarity can ensure that the matrix interference between the time-stacking volume curve and the time-cumulative storage curve is controlled within an acceptable range (e.g., slope difference ≤ 20%), avoiding the process of obtaining E being affected by interference factors. The comparison process based on the stacking overlap E and E0 is as follows:

[0104] If E is less than E0, it indicates that the overall shape similarity of the two curves is relatively small. This suggests a significant difference between the real-time cumulative storage and the corresponding target storage. In this case, a problem has occurred in the construction of the twin model, requiring an optimization notification. If E is greater than or equal to E0, it indicates that the overall shape similarity of the two curves is relatively large. This suggests that the actual cumulative storage and the corresponding target storage are relatively close in value, but R is still less than R2. This indicates a misalignment or delay between the data collection time node and the target time node in the target storage plan. This causes the calculated R values ​​to be less than the corresponding R2 when at the same time node. In this case, the target time nodes in the target storage plan can be corrected, and the target time nodes corresponding to the target storage are adjusted accordingly. It is understood that E0 is not specifically limited in this embodiment. In optional embodiments, a new E0 = 85% can be set to further reduce interference factors, as long as the corresponding processing is determined by comparing E and E0. The specific comparison process is described above.

[0105] Furthermore, the analysis module is also used to generate corresponding instructions based on the comparison result between the stacking overlap difference and the preset stacking overlap difference to determine the postponement of each time node in the target storage plan; the adjustment module is also used to control the target construction module to determine the postponement of each time node when constructing the target storage plan based on the instructions, and the interval of the postponement of the time node is positively correlated with the stacking overlap difference; wherein, the stacking overlap difference is the difference between the stacking overlap and the preset stacking overlap.

[0106] Specifically, in this embodiment, the stacking overlap difference C is the difference between the stacking overlap E and the preset stacking overlap E0. Therefore, by comparing C with the preset stacking overlap difference C0, the delay amount of the target time node in the target storage plan can be accurately determined. By adjusting the delay of the target time node, the cumulative storage amount can correspond to the target storage amount. After adjustment, the collection time node of the storage information no longer has a misalignment or delay with the target time node in the target storage plan, thereby optimizing the cargo storage process. In order to accurately determine the delay amount of the target time node, C0 can be divided into the first preset stacking overlap difference C1 and the second preset stacking overlap difference C2, setting C1=3% and C2=5%. The comparison process based on C with C1 and C2 is as follows:

[0107] If C is less than or equal to C1, the analysis module generates an instruction for the first time node adjustment coefficient. The adjustment module then adjusts all target time nodes in the target storage plan one time interval forward according to the instruction to ensure the cumulative storage volume corresponds to the target storage volume. If C is greater than C1 and less than or equal to C2, the analysis module generates an instruction for the second time node adjustment coefficient. The adjustment module then adjusts all target time nodes in the target storage plan two time intervals forward according to the instruction to ensure the cumulative storage volume corresponds to the target storage volume. If C is greater than C2, the analysis module generates an instruction for the first time node adjustment coefficient. The adjustment module then adjusts all target time nodes in the target storage plan three time intervals forward according to the instruction to ensure the cumulative storage volume corresponds to the target storage volume. It is understood that C1 and C2 are not specifically limited in this embodiment of the invention, and the above values ​​are not limited to these. Those skilled in the art can adjust C1 and C2 according to the actually generated target storage plan.

[0108] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0109] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An intelligent warehouse management system for an agricultural input platform, characterized in that, include: The goods receiving module is used to send receiving information; The goods outbound module is used to send outbound information; The cargo transportation module includes several AGVs used to transport goods within the warehouse; The information collection module is used to periodically collect storage information of each shelf in the warehouse; The model building module is used to obtain the storage information and build a twin model, and to obtain the inbound information and the outbound information and periodically update the corresponding storage data in the twin model, wherein there is a mapping relationship between the storage data and the corresponding storage information; A target construction module, which is connected to the goods inbound module and the goods outbound module respectively, is used to determine a target storage plan based on the inbound information and the outbound information. The target storage plan includes several time nodes and several target storage quantities corresponding to each time node. A statistics module, which is connected to the model building module, is used to periodically calculate the cumulative storage volume of goods in the warehouse based on the stored data; An analysis module, which is connected to the target construction module and the statistics module respectively, is used to determine whether the cargo storage process meets the standard based on the comparison between the cumulative storage amount and the target storage amount, and to generate instructions if the standard is not met. An adjustment module, which is connected to the analysis module, the cargo transportation module, the information collection module, and the target construction module respectively, is used to determine the detection frequency of the information collection module, the transportation speed of each AGV, the target storage plan, or each time node based on the instructions; The analysis module is also used to determine the reasons why the storage process of goods does not meet the standards based on the comparison results between the storage ratio difference and the preset storage ratio difference. The analysis module is also used to determine, based on the reasons, instructions to adjust the transport speed of each AGV, update the target storage plan, or redetermine the corresponding processing; The adjustment module is also used to adjust the transport speed of each AGV based on the instruction, or to update the target storage plan; Wherein, the storage ratio difference is the difference between the preset storage ratio and the storage ratio; The analysis module is also used to generate corresponding instructions based on the comparison results of stacking overlap degree and preset stacking overlap degree to determine to re-correct each of the time nodes, or to issue a notification to optimize the twin model. The adjustment module is also used to control the target construction module to determine and correct each of the time nodes based on the instructions, or to issue a notification to optimize the twin model; The analysis module is also used to calculate the stacking overlap based on the time-stack quantity curve and the time-cumulative storage curve.

2. The intelligent warehouse management system for agricultural input platforms according to claim 1, characterized in that, The analysis module is also used to determine whether the goods storage process meets the standard based on the comparison result of the storage ratio and the pre-stored preset storage ratio, or to determine whether the goods storage process meets the standard by combining the comparison result of the storage ratio at the current time node and the storage ratio at the previous time node. The analysis module is also used to determine the reason for non-compliance with standards based on the difference between the preset storage ratio and the storage ratio when it is determined that the goods storage process does not meet the standards. The storage ratio is the ratio of the cumulative storage amount to the corresponding target storage amount at the same time point.

3. The intelligent warehouse management system for the agricultural input platform according to claim 2, characterized in that, The analysis module is also used to determine whether to increase the detection frequency based on the comparison result of the storage ratio and the historical storage ratio; Wherein, the historical storage ratio is the storage ratio corresponding to the current time node, and the detection frequency is the frequency at which the information acquisition module collects the stored data.

4. The intelligent warehouse management system for agricultural input platforms according to claim 3, characterized in that, The analysis module is also used to generate corresponding instructions based on the comparison results between the periodic stacking difference and the pre-stored preset periodic stacking difference to determine the adjustment of the detection frequency; The adjustment module is also used to control the information acquisition module to increase the detection frequency based on the instruction, and the increase in the detection frequency is positively correlated with the period stacking difference; Wherein, the periodic stacking difference is the difference between the storage ratio and the historical storage ratio.

5. The intelligent warehouse management system for the agricultural input platform according to claim 1, characterized in that, The analysis module is also used to generate corresponding instructions based on the comparison results between the storage slope and the preset storage slope to adjust the transportation speed of each AGV. The adjustment module is also used to control the cargo transportation module to increase the transportation speed of each AGV based on the instruction, and the increase in transportation speed is positively correlated with the storage slope; The analysis module is further configured to construct a time-cumulative storage curve based on several time nodes and the cumulative storage amount corresponding to each time node, wherein the storage slope is the slope corresponding to the current time node on the time-cumulative storage curve.

6. The intelligent warehouse management system for the agricultural input platform according to claim 5, characterized in that, The analysis module is also used to generate corresponding instructions to increase the number of AGVs when the storage ratio difference is less than or equal to the preset storage ratio difference after the transportation speed of each AGV has been increased and adjusted. The adjustment module is also used to issue a notification to increase the number of AGVs scheduled based on the instruction, and the increased number of scheduling is positively correlated with the difference in storage capacity; Wherein, the storage difference is the difference between the target storage amount corresponding to the last time node within the preset detection period and the cumulative storage amount corresponding to the current time node.

7. The intelligent warehouse management system for the agricultural input platform according to claim 5, characterized in that, The analysis module is also used to determine whether to update the target storage plan based on the comparison result between the fitting overlap degree and the preset fitting overlap degree. The target construction module is also used to construct a time-heap size curve based on the target storage plan; The target construction module is also used to generate an outbound plan based on the outbound information, and to fit the target storage plan and the outbound plan to obtain a fitted plan and to construct an update time-stacking curve based on the fitted plan; The analysis module is further used to calculate the fitting overlap based on the time-stacking volume curve and the update time-stacking volume curve.

8. A smart warehousing management method for an agricultural input platform, characterized in that, The intelligent warehouse management system applied to the agricultural input platform as described in any one of claims 1-7 includes: Send inbound information through the goods inbound module; Send outbound information through the goods outbound module; Goods are transported in the warehouse using a cargo transport module that includes several AGVs; The storage information corresponding to each shelf in the warehouse is periodically collected by the information collection module; The storage information is obtained through the model building module and a twin model is constructed. The storage data in the twin model is periodically updated based on the inbound information and the outbound information, wherein there is a mapping relationship between the storage data and the corresponding storage information. The target construction module, which is connected to the goods inbound module and the goods outbound module respectively, determines the corresponding target storage plan based on the inbound information and the outbound information. The target storage plan includes several time nodes and several target storage quantities corresponding to each time node. The statistical module, connected to the information collection module, periodically calculates the cumulative storage volume of goods in the warehouse based on the stored data. An analysis module, which is connected to the target construction module and the statistics module respectively, determines whether the cargo storage process meets the standard based on the cumulative storage amount and the target storage amount, and generates an instruction when it is determined that the standard is not met. The adjustment module, which is connected to the analysis module, the cargo transportation module, the information collection module, and the target construction module respectively, determines the detection frequency of the information collection module, the transportation speed of each AGV, the target storage plan, or the time nodes based on the instructions.

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