Intelligent inventory method and system for fertilizer storage

Abstract

This invention relates to the fields of warehousing logistics and intelligent inventory technology, and particularly to an intelligent inventory method and system for fertilizer storage. The method includes the following steps: First, a point cloud of the fertilizer pile is acquired using 3D scanning and then discretized and modeled. Second, based on soil mechanics and powder engineering theory, the nonlinear vertical gradient distribution of porosity and bulk density is calculated using a compression index model according to the self-weight stress of the pile. Next, an environmental correction factor is constructed by combining environmental humidity and storage time to dynamically compensate for local density. Finally, by integrating and summing the corrected density field, the total inventory of the entire warehouse is accurately calculated. This invention can accurately quantify the combined influence of physical compaction and environmental factors on fertilizer density, significantly improving the inventory accuracy of bulk fertilizer.

Description

Technical Field

[0001] This invention relates to the field of warehousing and logistics and intelligent inventory technology, and in particular to an intelligent inventory method and system for fertilizer storage. Background Technology

[0002] In the fertilizer production and distribution process, the warehousing and inventory management of bulk fertilizers, including compound fertilizers, urea, potash fertilizers, and phosphate fertilizers, is the core of inventory management, directly impacting enterprise production planning, supply chain scheduling, and capital turnover efficiency. Unlike packaged goods, bulk fertilizers, as typical granular materials, have significant unique properties: they easily absorb moisture from the air, leading to hygroscopic adhesion; they are prone to clumping under storage pressure; and their particle hardness and shape vary. Combined with their inherent compressibility, these characteristics pose challenges to the accuracy of inventory counting.

[0003] Traditional inventory management systems often use the static volumetric method for bulk materials such as coal and ore, estimating inventory quality by multiplying the volume of the stockpile by a fixed average bulk density. However, this method has significant fundamental flaws when applied to fertilizer storage: First, fertilizer particles exhibit creep characteristics; under the weight of the stack, the bottom particles are compressed and rearranged, reducing porosity and resulting in a non-linear density gradient distribution along the depth, leading to significant calculation errors due to the fixed density assumption. Second, fertilizer stockpiles are sensitive to humidity and temperature; moisture absorption during storage increases mass, and long-term storage can cause particle bridging and clumping, leading to changes in the stockpile structure, which the traditional volume-to-density method cannot capture.

[0004] Current technologies generally overlook the dynamic evolution of density caused by physical compaction and environmental factors, resulting in significant discrepancies between inventory data after long-term storage and actual outbound data. This leads to inconsistencies between inventory records and actual stock levels, impacting production and logistics scheduling. Therefore, there is an urgent need for an intelligent inventory management method that can accurately quantify the physical compaction mechanism and environmental impact to address the industry pain point of inaccurate bulk fertilizer inventory accounting. Summary of the Invention

[0005] To address the problem that existing static volumetric methods cannot adapt to the hygroscopic agglomeration and creep settling characteristics of bulk fertilizers, resulting in low inventory accuracy, this invention provides an intelligent inventory method and system for fertilizer storage.

[0006] In a first aspect, the present invention provides an intelligent inventory method for fertilizer storage, employing the following technical solution:

[0007] A smart inventory method for fertilizer storage includes the following steps:

[0008] Point cloud data of the fertilizer pile surface is obtained using a 3D scanning device, a 3D mesh model is constructed, and the 3D mesh model is discretized into several independent vertical metering units. At the same time, the initial physical property parameters of the current batch of fertilizer are matched according to the storage record.

[0009] Based on the initial physical property parameters, the cumulative self-weight stress varying with depth inside each vertical metering unit is calculated using the soil mechanics consolidation theory. The cumulative self-weight stress is mapped to the nonlinear vertical gradient porosity according to the compression index model, and the basic wet density of each vertical metering unit is derived based on the vertical gradient porosity.

[0010] The real-time humidity data and storage time of the storage environment are obtained. The environmental correction factor is calculated using the moisture absorption weight gain function. The base wet density is multiplied and corrected using the environmental correction factor to obtain the corrected density that includes the dual effects of physical compaction and environmental moisture absorption.

[0011] The corrected density within each vertical metering unit is integrated along the height direction to obtain the individual mass, and the total inventory of the entire warehouse is calculated to achieve intelligent inventory management of fertilizer storage.

[0012] This invention treats fertilizer piles as dynamically evolving granular systems, enabling precise capture of nonlinear density gradients caused by their own weight and fine-tuning of density due to environmental moisture absorption, thereby significantly improving the accuracy of inventory and accounting.

[0013] Preferably, the step of matching the initial physical property parameters of the current batch of fertilizer according to the warehousing record specifically includes:

[0014] Get the current batch ID of the fertilizer pile entering the warehouse;

[0015] Based on the batch ID of the incoming materials, query the preset material attribute database and extract the initial physical attribute parameters corresponding to that batch of fertilizer.

[0016] This invention ensures the accuracy of data acquisition by automatically matching material properties, avoiding errors caused by manual parameter entry.

[0017] Preferably, the initial physical property parameters include at least: the initial loose porosity of the fertilizer, the true density of the solid particles, and the initial moisture content.

[0018] Preferably, the method for obtaining the vertical gradient porosity is as follows:

[0019]

[0020] In the formula, For depth Vertical gradient porosity at that location The initial loose porosity is one of the initial physical property parameters. The compression index, For depth The cumulative self-weight stress at the location, Characteristic yield stress, This represents the logarithmic function with base 10.

[0021] This invention introduces the compression index model from powder engineering, which can accurately describe the physical law that the porosity of fertilizer particles decreases logarithmically under pressure, and constructs a closed-loop dependence relationship between stress and density, effectively solving the problem of underestimation of the density of the lower layer.

[0022] Preferably, the method for obtaining the cumulative self-weight stress is as follows: using a top-down layered recursive method, the vertical measuring unit is divided into several micro-element layers along the height direction;

[0023] For any given layer, the cumulative self-weight stress of that layer is obtained by summing and integrating the local moisture density of all the micro-element layers above it along the direction of gravity.

[0024] Preferably, the computing environment correction factor specifically includes:

[0025] Based on the difference between real-time humidity data and the critical humidity of the fertilizer, the moisture absorption driving term is determined;

[0026] By combining the storage time, an environmental correction factor that dynamically changes over time is calculated using a progressive saturation model to characterize the weight gain ratio of fertilizer after moisture absorption.

[0027] Preferably, the environmental correction factor satisfies the following relationship:

[0028]

[0029] In the formula, As an environmental correction factor, To achieve the maximum moisture absorption weight gain ratio, Let be the moisture absorption rate constant. For real-time humidity data, This is the critical humidity level for fertilizer. The storage duration is represented by e, which is a natural constant.

[0030] This invention analyzes the effects of environmental humidity and time on fertilizer density, accurately describing the density changes of fertilizer due to moisture absorption in an unsealed environment, so that the model still has high robustness under different seasons and temperature and humidity conditions.

[0031] Preferably, the baseline wet density is corrected multiplicatively using an environmental correction factor, specifically including:

[0032] The corrected density is obtained by multiplying the density value at any depth in the basic wet density by the environmental correction factor.

[0033] Preferably, the construction of the three-dimensional mesh model and the discretization of the three-dimensional mesh model into several independent vertical measurement units include:

[0034] A three-dimensional mesh model is obtained by processing it using a mesh mapping algorithm. The three-dimensional mesh model is then discretized to obtain several regular vertical measurement units.

[0035] This invention uses orthogonal meshes and slice discretization to adapt to fertilizer piles of different shapes, thereby accurately obtaining the volume information of the fertilizer pile.

[0036] Secondly, the present invention provides an intelligent inventory system for fertilizer storage, which adopts the following technical solution:

[0037] An intelligent inventory system for fertilizer storage includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the aforementioned intelligent inventory method for fertilizer storage is implemented.

[0038] By adopting the above technical solution, a computer program for the intelligent inventory method of fertilizer storage is generated and stored in a memory so that it can be loaded and executed by a processor. This allows for the creation of a terminal device based on the memory and processor, making it convenient to use.

[0039] The present invention has the following technical effects:

[0040] This invention, by introducing soil mechanics consolidation theory and compression index model, can accurately capture the nonlinear density gradient phenomenon of upper loose and lower tight caused by self-weight compaction inside the bulk fertilizer pile, effectively eliminating the problem of serious underestimation of inventory caused by neglecting the deep densification effect in traditional methods.

[0041] This invention introduces a moisture absorption weight gain function that dynamically changes with the storage time, which can calculate and compensate for the nonlinear mass growth caused by the intrusion of environmental moisture in real time. This makes the inventory results not only reflect the physical volume, but also truly reflect the actual physical mass under the current environmental conditions, significantly improving the robustness of inventory under different seasons and storage conditions. Attached Figure Description

[0042] Figure 1 This is a flowchart of a method in an intelligent inventory method for fertilizer storage provided in an embodiment of the present invention;

[0043] Figure 2 This is a schematic diagram of the corrected density provided in an embodiment of the present invention;

[0044] Figure 3 This is a schematic diagram of the calculation of relative error provided in an embodiment of the present invention. Detailed Implementation

[0045] This invention discloses an intelligent inventory method for fertilizer storage, referring to... Figure 1This includes steps S1-S4:

[0046] S1: Use non-contact 3D scanning equipment to acquire point cloud data of the fertilizer pile surface, construct a 3D mesh model, and discretize the 3D mesh model into several independent vertical metering units. At the same time, match the initial physical property parameters of the current batch of fertilizer according to the storage record.

[0047] It should be noted that in order to achieve accurate and dynamic inventory of bulk fertilizer stock, precise information reflecting the geometry of the stockpile needs to be extracted from the complex warehouse environment.

[0048] It should be further noted that bulk fertilizers, such as urea and compound fertilizers, are not absolutely rigid bodies. Their surface morphology undergoes slight settling as particles rearrange, and different batches of fertilizer have different angles of repose and stacking characteristics. This irregular deformation characteristic means that traditional overall estimation methods are prone to inventory errors due to neglecting changes in the pile's geometry. In this situation, if conventional manual measurement or simple geometric approximations are used, the collected pile geometry is easily distorted due to insufficient resolution and one-size-fits-all parameters. Therefore, to collect more accurate pile geometry information, more refined data acquisition methods are required.

[0049] Preferably, as an example, point cloud data of the fertilizer pile surface is acquired using a non-contact 3D scanning device, a 3D mesh model is constructed, and the 3D mesh model is discretized into several independent vertical metering units. Simultaneously, the initial physical property parameters of the current batch of fertilizer are matched according to the storage records, including:

[0050] The system takes raw point cloud data obtained by LiDAR scanning installed on warehouse walkways or vehicles as input, processes it using a mesh mapping algorithm to obtain a three-dimensional mesh model, and discretizes the three-dimensional mesh model to obtain several regular vertical measurement units and their corresponding real-time height data.

[0051] Simultaneously, the system obtains the batch ID of the current fertilizer pile; based on the batch ID, it queries the preset material attribute database and extracts the initial physical attribute parameters corresponding to the batch of fertilizer pile. The initial physical attribute parameters include: initial loose porosity, true density of solid particles, and initial moisture content.

[0052] S2: Based on the initial physical property parameters, the cumulative self-weight stress varying with depth inside each vertical metering unit is calculated using the soil mechanics consolidation theory. The cumulative self-weight stress is mapped to a nonlinear vertical gradient porosity based on the compression index model, and the basic wet density of each vertical metering unit is derived from the vertical gradient porosity.

[0053] It should be noted that in order to accurately estimate the internal density of bulk fertilizer, it is necessary to extract the internal density that reflects the internal compaction state in the absence of internal sensors.

[0054] It should be further explained that fertilizer granule piles are granular media. In the piled state, the gravity of the upper layer of fertilizer acts on the lower layer, leading to an increase in contact points between particles and pore compression. This mechanical characteristic means that the porosity and density inside the pile are no longer uniformly distributed, but exhibit a nonlinear gradient distribution characteristic of decreasing porosity and increasing density with increasing depth. This causes a systematic negative bias in the simple volume multiplication by average bulk density algorithm. In this case, if the conventional fixed density method is used, it is easy to misjudge the actual inventory as being much larger than the book inventory by ignoring the compaction weight gain effect at the bottom. Therefore, in order to more accurately reflect the fertilizer density, this invention uses a porosity evolution model based on effective stress to infer fertilizer density information.

[0055] Preferably, as an example, based on initial physical property parameters, the cumulative self-weight stress varying with depth within each vertical metering unit is calculated using soil consolidation theory. The cumulative self-weight stress is then mapped to a nonlinear vertical gradient porosity using a compression index model. The base wet density of each vertical metering unit is then derived from the vertical gradient porosity, including:

[0056] First, the vertical measurement unit is discretized into several micro-elements along the height direction.

[0057] Then, for the topmost layer, its effective overburden stress is set to 0, at which point the porosity of this layer is equal to the initial loose porosity. For any given layer, the sum of the weights of all layers above it is first calculated to obtain the cumulative self-weight stress borne by that layer;

[0058] Subsequently, the accumulated self-weight stress was substituted into the compression index model to calculate the vertical gradient porosity of the layer.

[0059] Finally, based on the physical relationship between porosity and density, the basic wet density of this layer was derived. Through this layer-by-layer recursive approach, the creep and sedimentation characteristics of the fertilizer were accurately measured.

[0060] The above embodiments involve vertical gradient porosity and basic wet density. The methods for determining vertical gradient porosity and basic wet density will be explained below.

[0061] The method for calculating vertical gradient porosity is as follows:

[0062]

[0063] In the formula, For depth Vertical gradient porosity, The initial loose porosity; For example, the compression index is... Take 0.08; Characteristic yield stress, exemplarily, Take 1000 Pa; For depth The cumulative self-weight stress at the location, This represents the logarithmic function with base 10. This is a compression index model.

[0064] The method for calculating basic wet density is as follows:

[0065]

[0066] In the formula, For depth The basic wet density at that location, The true density of the solid particles. This represents the initial moisture content.

[0067] Understandably, the first term in the relation... The first term characterizes the natural loose state of the fertilizer, while the second term, with its logarithmic decay, reflects the compaction effect of external forces. When the physical phenomenon of self-weight compaction occurs, the depth... Increased stress will lead to cumulative self-weight stress. The increase leads to an increase in the vertical gradient porosity in the formula. A logarithmic decreasing trend occurs, thereby forcing the base wet density to decrease. It increases with depth.

[0068] The above formula can accurately describe the nonlinear compaction mechanism of bulk fertilizer and accurately describe the distribution density of fertilizer, effectively eliminating the estimation error risk caused by the fixed density method.

[0069] It should be noted that, considering that some fragile or powdery fertilizers have significant time-dependent rheological characteristics, that is, they gradually become denser over time, the derivation of the vertical gradient porosity can also be achieved using a secondary consolidation creep model based on rheological characteristics.

[0070] Alternatively, as another implementation, the vertical gradient porosity can be calculated in the following manner:

[0071] In this scenario, a time creep term needs to be added to the compression exponential model. Porosity is corrected, among which For storage time, For reference time, The consolidation coefficient is denoted by .

[0072] Understandably, although this method increases computational complexity, it can describe the characteristic of porosity continuously decreasing with resting time under constant pressure, and can also more accurately reflect the real-time porosity distribution, thus achieving a precise fit to the natural settling process of long-term stored fertilizer.

[0073] S3: Obtain real-time humidity data and storage time of the storage environment, calculate the environmental correction factor using the moisture absorption weight gain function, and use the environmental correction factor to multiply and correct the basic wet density to obtain the corrected density that includes the dual effects of physical compaction and environmental moisture absorption.

[0074] It should be noted that since environmental humidity also affects fertilizer density, in order to accurately capture fertilizer density, it is necessary to extract correction information that reflects the environmental impact based on physical compaction.

[0075] It should be further noted that bulk fertilizers are mostly hygroscopic, and in non-sealed storage environments, they will exchange moisture with the air. When the relative humidity of the environment exceeds the fertilizer's critical relative humidity, the fertilizer will absorb moisture and increase in weight, and may even deliquesce or clump. This environmental characteristic means that the density of the fertilizer is not constant, but rather exhibits dynamic changes with environmental humidity and time. In this situation, ignoring environmental factors can easily lead to deviations in quality accounting. Therefore, to better adapt to this environmentally sensitive characteristic, this invention employs an environmental correction factor.

[0076] Preferably, as an example, real-time humidity data and storage time of the storage environment are obtained, an environmental correction factor is calculated using a moisture absorption weight gain function, and the base wet density is multiplicatively corrected using the environmental correction factor to obtain a corrected density that includes the dual effects of physical compaction and environmental moisture absorption, including:

[0077] First, sensors are used to collect real-time humidity data of the storage environment and to obtain the storage time of the fertilizer.

[0078] Next, an environmental correction factor is calculated based on real-time humidity data and storage time. The specific calculation method is as follows:

[0079]

[0080] In the formula, As an environmental correction factor, For the maximum moisture absorption weight gain ratio, exemplarily, Take 0.05; It is the moisture absorption rate constant; This provides real-time humidity data. This is the critical humidity level for the fertilizer. It is a moisture-absorbing driving element; Storage duration; It is a natural constant. The function of moisture absorption and weight gain. It is an asymptotic saturation model.

[0081] Finally, the baseline wet density is corrected using an environmental correction factor. The specific implementation method is as follows:

[0082]

[0083] In the formula, For depth The corrected density at that location.

[0084] It is understandable that in the relational expression This characterizes the situation where moisture absorption tends to saturate over time. Reflects the moisture absorption drive; when the ambient humidity... When the humidity is greater than the critical humidity, It will last for time The value gradually increases and becomes greater than 1, thereby positively correcting the density and simulating the phenomenon of weight gain due to moisture absorption.

[0085] The above formulas can accurately describe the impact of environmental factors on fertilizer density, thus providing a basis for accurate subsequent calculation of fertilizer quality.

[0086] Figure 2 This is a revised density diagram. The X-axis represents the width of the pile cross-section, and the Y-axis represents the packing depth. The heat map describes the density distribution of slices inside the pile, with the density at the bottom of the pile being high, approaching 1000. Above, the density at the top is low, with a value close to 600. Images can visually demonstrate the nonlinear density gradient caused by self-weight compaction.

[0087] S4: Integrate the corrected density along the height direction within each vertical metering unit to obtain the individual mass, and sum and calculate the total inventory of the entire warehouse to achieve intelligent inventory management of fertilizer storage.

[0088] It should be noted that the indices reflecting density and volume have already been obtained in the above steps, and the inventory quality can be calculated based on density and volume below.

[0089] Preferably, as an example, the corrected density within each vertical metering unit is integrated along the height direction to obtain the unit mass, and the total inventory of the entire warehouse is calculated to achieve intelligent inventory management of fertilizer storage, including:

[0090]

[0091]

[0092] In the formula, grid coordinates The individual mass of the vertical metering unit at the location; Total inventory quality of the entire warehouse; This is the cross-sectional area of ​​the vertical metering unit; This represents the real-time stacking height of the vertical metering unit; For depth Corrected density at the location; For the horizontal and vertical coordinate indexes of the vertical measurement units, This represents the number of rows in the vertical metering unit. This represents the number of columns for the vertical measurement units.

[0093] Understandable It reflects the density at various depths. By integrating the density at all depths, the true mass of the deep layers of the pile can be captured.

[0094] In this way, the above operations can achieve an accurate inventory of bulk fertilizer stock.

[0095] Figure 3 The diagram illustrates the relative error calculation, with the X-axis representing storage time and the Y-axis representing the relative error. The image clearly shows that the error of the traditional method climbed from an initial approximately 14.9% to over 22%, while the error of this proposed method remained consistently controlled at an extremely low level of 1% to 1.8%. This strongly demonstrates that this method not only boasts high accuracy but also exhibits exceptional long-term monitoring stability.

[0096] This invention also discloses an intelligent inventory system for fertilizer storage, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement an intelligent inventory method for fertilizer storage according to this invention.

[0097] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0098] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as resistive random access memory (DRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (DRAM), high-bandwidth memory, hybrid memory cube, etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device.

Claims

1. An intelligent inventory method for fertilizer storage, characterized in that, Including the following steps: Point cloud data of the fertilizer pile surface is obtained using a 3D scanning device, a 3D mesh model is constructed, and the 3D mesh model is discretized into several independent vertical metering units. At the same time, the initial physical property parameters of the current batch of fertilizer are matched according to the storage record. Based on the initial physical property parameters, the cumulative self-weight stress varying with depth within each vertical metering unit is calculated using soil consolidation theory. A top-down layered recursive method is adopted to divide the vertical metering unit into several micro-layers along the height direction. For any layer, the local wet density of all micro-layers above it is accumulated and integrated along the gravity direction to obtain the cumulative self-weight stress of that layer. The cumulative self-weight stress is mapped to a nonlinear vertical gradient porosity based on the compression index model, and the basic wet density of each vertical metering unit is derived based on the vertical gradient porosity. The system acquires real-time humidity data and storage duration of the storage environment, and calculates environmental correction factors using a moisture absorption weight gain function. This includes: determining the moisture absorption driving term based on the difference between real-time humidity data and the critical humidity of the fertilizer; calculating the environmental correction factor that dynamically changes over time using a progressive saturation model in conjunction with the storage duration, which is used to characterize the weight gain ratio of the fertilizer after moisture absorption; and using the environmental correction factor to multiply and correct the base wet density to obtain the corrected wet density that includes the dual effects of physical compaction and environmental moisture absorption. The corrected wet density in each vertical metering unit is integrated along the height direction to obtain the individual mass, and the total inventory of the entire warehouse is calculated to achieve intelligent inventory management of fertilizer storage.

2. The intelligent inventory method for fertilizer storage according to claim 1, characterized in that, The process of matching the initial physical property parameters of the current batch of fertilizer based on the warehousing record specifically includes: Get the current batch ID of the fertilizer pile entering the warehouse; Based on the batch ID of the incoming materials, query the preset material attribute database and extract the initial physical attribute parameters corresponding to that batch of fertilizer.

3. The intelligent inventory method for fertilizer storage according to claim 1, characterized in that, The initial physical property parameters include at least the initial loose porosity of the fertilizer, the true density of the solid particles, and the initial moisture content.

4. The intelligent inventory method for fertilizer storage according to claim 1, characterized in that, The method for obtaining the vertical gradient porosity is as follows: In the formula, For depth Vertical gradient porosity at that location The initial loose porosity is one of the initial physical property parameters. The compression index, For depth The cumulative self-weight stress at the location, Characteristic yield stress, This represents the logarithmic function with base 10.

5. The intelligent inventory method for fertilizer storage according to claim 1, characterized in that, The environmental correction factor satisfies the following relationship: In the formula, As an environmental correction factor, To achieve the maximum moisture absorption weight gain ratio, Here is the moisture absorption rate constant. For real-time humidity data, This is the critical humidity level for fertilizer. The storage duration is represented by e, which is a natural constant.

6. The intelligent inventory method for fertilizer storage according to claim 1, characterized in that, The baseline wet density is corrected multiplicatively using an environmental correction factor, specifically including: The corrected wet density is obtained by multiplying the density value at any depth in the basic wet density by the environmental correction factor.

7. The intelligent inventory method for fertilizer storage according to claim 1, characterized in that, The construction of the three-dimensional mesh model, and the discretization of the three-dimensional mesh model into several independent vertical measurement units, includes: A three-dimensional mesh model is obtained by processing it using a mesh mapping algorithm. The three-dimensional mesh model is then discretized to obtain several regular vertical measurement units.

8. An intelligent inventory system for fertilizer storage, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement an intelligent inventory method for fertilizer storage according to any one of claims 1-7.

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