A method and system for monitoring a warehouse environment for malt dextrin

Abstract

This invention discloses a method and system for monitoring the storage environment of maltodextrin, relating to the field of storage environment monitoring technology. The method involves synchronously collecting ambient temperature and relative humidity data at each sampling point at a preset frequency to obtain spatiotemporal temperature and humidity data for each sampling point. A moisture potential difference network is constructed based on the spatiotemporal temperature and humidity data of all sampling points. Based on the moisture potential difference network, the moisture migration state between the maltodextrin material and the storage environment is calculated to obtain moisture migration parameters. Barrier characteristic parameters of the maltodextrin packaging are obtained, and the equivalent moisture increment is determined based on these parameters. A warehouse-wide clumping risk index field is constructed based on the temperature and humidity spatiotemporal dataset, moisture migration parameters, and equivalent moisture increment. Storage temperature and humidity are then controlled based on this warehouse-wide clumping risk index field. This solution achieves synchronous monitoring of temperature and humidity throughout the warehouse, accurate quantification of moisture migration state, and considers the impact of packaging barrier performance, thus improving the efficiency of storage environment monitoring.

Description

Technical Field

[0001] This invention belongs to the field of warehouse environment monitoring technology, specifically relating to a method and system for monitoring the warehouse environment of maltodextrin. Background Technology

[0002] Maltodextrin, a widely used polysaccharide powder raw material, is extensively applied in food, pharmaceuticals, and animal feed due to its excellent solubility, filling properties, and stability, making it an indispensable basic raw material in industrial production. However, maltodextrin is an amorphous powder with strong hygroscopic properties, making it highly susceptible to clumping during storage due to changes in ambient temperature and humidity. This clumping severely affects product quality and subsequent processing performance, and can even lead to raw material waste and economic losses. Therefore, precise monitoring and control of the storage environment are crucial for ensuring the quality of stored maltodextrin.

[0003] The clumping mechanism of maltodextrin is complex, mainly related to various factors such as ambient temperature and humidity, moisture migration between the material and the environment, and the barrier properties of packaging. When the temperature and humidity of the storage environment fluctuate significantly, moisture migration will occur between the maltodextrin material and the environment. If the material's moisture absorption or desorption process continues, it will lead to the formation of crystal bridges or plastic deformation on the surface of the material particles, thereby causing particle agglomeration and clumping. At the same time, the difference in the moisture barrier properties of maltodextrin packaging will directly affect the amount of moisture from the environment penetrating into the material, further exacerbating the risk of clumping.

[0004] Currently, the monitoring of maltodextrin storage environment mostly adopts traditional manual inspection or single-point temperature and humidity monitoring methods, which can only obtain temperature and humidity data of local areas within the storage space. It is impossible to achieve comprehensive temperature and humidity throughout the entire warehouse, making it difficult to predict the risk of maltodextrin clumping in advance, resulting in low efficiency of maltodextrin environmental monitoring. Summary of the Invention

[0005] The purpose of this invention is to solve the problem of the difficulty in predicting the risk of maltodextrin clumping in advance, which leads to low efficiency in maltodextrin environmental monitoring, and to propose a method and system for monitoring the storage environment of maltodextrin.

[0006] In a first aspect of this invention, a method for monitoring the storage environment of maltodextrin is first proposed, the method comprising: The ambient temperature and relative humidity data of each sampling point are collected synchronously at a preset frequency to obtain the spatiotemporal data of temperature and humidity for each sampling point. A moisture potential difference network is constructed based on the spatiotemporal data of temperature and humidity corresponding to all sampling points; Based on the moisture potential difference network, the moisture migration state between the maltodextrin material and the storage environment is calculated to obtain the moisture migration parameters. Obtain the barrier characteristic parameters of maltodextrin packaging, and determine the equivalent moisture increment based on the barrier characteristic parameters; Based on the aforementioned temperature and humidity spatiotemporal dataset, moisture migration parameters, and equivalent moisture increment, a full-warehouse clumping risk index field is constructed. Warehouse temperature and humidity are controlled based on the aforementioned full-warehouse clumping risk index field.

[0007] This invention provides a method for monitoring the storage environment of maltodextrin. It acquires spatiotemporal temperature and humidity data by synchronously collecting temperature and humidity data from various sampling points at a preset frequency. This data is then used to construct a moisture potential difference network to calculate moisture migration parameters. Combined with the barrier properties of maltodextrin packaging, the equivalent moisture increment is determined. This allows for the construction of a whole-warehouse clumping risk index field, which is then used to regulate storage temperature and humidity. This method enables comprehensive synchronous monitoring of temperature and humidity across the entire warehouse, precise quantification of moisture migration between materials and the environment, and consideration of the impact of packaging barrier performance on clumping risk. It can accurately locate clumping risks in different areas of the warehouse and implement targeted temperature and humidity control. This effectively solves the problems of limited data collection range, inaccurate risk assessment, and coarse regulation inherent in traditional monitoring methods. Therefore, it enables early prediction of maltodextrin clumping risk and improves the efficiency of maltodextrin environmental monitoring.

[0008] Optionally, the temperature and humidity acquisition nodes are divided into a first type of node and a second type of node; the first type of node is a temperature and humidity sensor attached to the surface of the maltodextrin packaging, used to collect the surface temperature and relative humidity of the material; the second type of node is a temperature and humidity sensor deployed in the storage space, used to collect the temperature and relative humidity inside the storage space; each first type of node has one second type of node within a preset range, forming a node pair.

[0009] Optionally, constructing a moisture potential difference network based on the spatiotemporal data of temperature and humidity corresponding to all sampling points includes: A data acquisition network is constructed for the locations corresponding to all second-type nodes in the aforementioned temperature and humidity spatiotemporal dataset; Through formula group The environmental moisture potential corresponding to each second-type sampling point was calculated. and the material moisture potential corresponding to each first-type sampling point ; Where ln is the natural logarithm, RH is the relative humidity inside the warehouse, and T is the temperature inside the warehouse. This is a specific gas constant for water vapor. Here, ERH represents the surface temperature of the material, and ERH represents the relative humidity of the material surface. For each node pair, the difference between the corresponding environmental moisture potential and the material moisture potential is calculated to obtain the moisture potential difference. An initial water potential difference network is constructed based on the water potential difference corresponding to the location of the second type of sampling point in all node pairs; Based on the data acquisition network, the initial water potential difference network is expanded to obtain a water potential difference network.

[0010] Optionally, based on the data acquisition network, the initial water potential difference network is expanded to obtain a water potential difference network including: The nodes in the initial water potential difference network are used as known sample points to update the nodes of the data acquisition network to obtain the initial data update network. For each unpaired first-type node in the initial data update network, if there are known sample points within a preset distance of the node, the moisture potential difference of the node is estimated by weighting the moisture potential difference of the known sample points within the preset distance in inverse square proportion. If there is no known sample point within the preset distance of the node, the water potential difference of the node is calculated by using the nearest known sample point of the node through a multiple linear regression model. The water potential difference network is obtained by determining the water potential difference of each node in the initial data update network.

[0011] Optionally, based on the moisture potential difference network, the moisture migration parameters obtained by calculating the moisture migration state between the maltodextrin material and the storage environment include: For each node in the moisture potential difference network, the moisture migration state between the maltodextrin material and the storage environment is determined based on the moisture potential difference of that node; the migration state includes hygroscopic state, desorption state and equilibrium state. If a node's migration state is not in equilibrium, then the formula is used. Calculate the rate of water migration; in, This represents the water migration rate value. This represents the water potential difference value. The baseline migration rate value, This is a temperature correction factor; Through formula Calculate the cumulative migration amount; in, To update the cumulative migration volume, This represents the historical cumulative migration volume. When greater than 0 =1, When less than 0 -1, When equal to 0 =0, Preset frequency; The water migration parameters are obtained by determining the water migration direction, water migration rate, and cumulative migration amount at each node.

[0012] Optionally, obtaining the barrier characteristic parameters of the maltodextrin packaging, and determining the equivalent moisture increment based on the barrier characteristic parameters, includes: The barrier properties of maltodextrin packaging were determined in advance through experiments. The barrier properties included water vapor permeability P, surface area of ​​a single package S, and mass of a single package of maltodextrin m. For each node, calculate the absolute value of the temperature difference between the temperature inside the warehouse and the surface temperature of the material. Through formula Calculate the equivalent water increment corresponding to the first type of node in each node pair; in, The absolute value of the temperature difference. This is the equivalent temperature correction factor. This is the preset frequency.

[0013] Optionally, based on the aforementioned temperature and humidity spatiotemporal dataset, moisture migration parameters, and equivalent moisture increment, a full-warehouse clumping risk index field is constructed, including: A three-dimensional spatial grid is constructed based on the aforementioned warehouse space; The temperature and humidity data and moisture migration parameters collected by each second type of node are assigned to the three-dimensional spatial grid. For grid points that are not covered by nodes, a preset coverage algorithm is used to supplement them to obtain the basic environmental temperature and humidity field and moisture migration field covering the entire warehouse. The equivalent moisture increment corresponding to each first type node is mapped to the three-dimensional spatial grid, and the equivalent moisture increment is diffused based on the preset infiltration diffusion distance to obtain the infiltration risk field. The basic environmental temperature and humidity field, the moisture migration field, and the infiltration risk field are weighted and fused to obtain the overall warehouse clumping risk index field.

[0014] Optionally, the weighted fusion of the basic environmental temperature and humidity field, the moisture migration field, and the permeation risk field to obtain the overall warehouse clumping risk index field includes: For each grid point, using the formula Calculate the risk index for each grid point; in The weighting coefficients are the basic environmental temperature and humidity fields. These are the weighting coefficients for the water migration field. These are the weighting coefficients for the penetration risk field. For the third in the three-dimensional spatial mesh The temperature and humidity of the grid, For the third in the three-dimensional spatial mesh Mesh moisture migration parameters For the third in the three-dimensional spatial mesh The equivalent moisture increment of the grid.

[0015] In a second aspect of this invention, a maltodextrin storage environment monitoring system is provided, comprising: The temperature and humidity data acquisition module is used to synchronously acquire ambient temperature data and relative humidity data of each sampling point at a preset frequency to obtain the spatiotemporal temperature and humidity data corresponding to each sampling point. The moisture potential difference network construction module is used to construct a moisture potential difference network based on the spatiotemporal data of temperature and humidity corresponding to all sampling points. The moisture migration parameter determination module is used to calculate the moisture migration state between the maltodextrin material and the storage environment based on the moisture potential difference network to obtain the moisture migration parameters. The equivalent moisture increment determination module is used to obtain the barrier characteristic parameters of maltodextrin packaging and determine the equivalent moisture increment based on the barrier characteristic parameters. The whole-warehouse clumping risk index field construction module is used to construct the whole-warehouse clumping risk index field based on the temperature and humidity spatiotemporal dataset, moisture migration parameters and equivalent moisture increment; The warehouse environment monitoring module is used to regulate the temperature and humidity of the warehouse based on the overall warehouse clumping risk index field.

[0016] The beneficial effects of this invention are: This invention proposes a method for monitoring the storage environment of maltodextrin. It acquires spatiotemporal temperature and humidity data by synchronously collecting temperature and humidity data from various sampling points at a preset frequency. This data is then used to construct a moisture potential difference network to calculate moisture migration parameters. Combined with the barrier properties of maltodextrin packaging, the equivalent moisture increment is determined. This leads to the construction of a whole-warehouse clumping risk index field, which is used to regulate storage temperature and humidity. This method enables comprehensive synchronous monitoring of temperature and humidity throughout the warehouse, precise quantification of moisture migration between materials and the environment, and consideration of the impact of packaging barrier performance on clumping risk. It can accurately locate clumping risks in different areas of the warehouse and implement targeted temperature and humidity control. This effectively solves the problems of limited data collection range, inaccurate risk assessment, and coarse control in traditional monitoring methods, thereby enabling early prediction of maltodextrin clumping risk and improving the efficiency of maltodextrin environmental monitoring. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 A flowchart illustrating a method for monitoring the storage environment of maltodextrin provided in an embodiment of the present invention; Figure 2 This is a framework diagram of a maltodextrin storage environment monitoring system provided in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] This invention provides a method for monitoring the storage environment of maltodextrin. See also... Figure 1 , Figure 1 A flowchart illustrating a method for monitoring the storage environment of maltodextrin according to an embodiment of the present invention. The method includes the following steps: S101 synchronously collects ambient temperature and relative humidity data of each sampling point at a preset frequency to obtain the spatiotemporal temperature and humidity data of each sampling point. S102, construct a moisture potential difference network based on the spatiotemporal data of temperature and humidity corresponding to all sampling points; S103, based on the moisture potential difference network, calculate the moisture migration state between maltodextrin material and the storage environment to obtain moisture migration parameters. S104, Obtain the barrier characteristic parameters of maltodextrin packaging, and determine the equivalent moisture increment based on the barrier characteristic parameters; S105, based on the spatiotemporal dataset of temperature and humidity, moisture migration parameters and equivalent moisture increment, constructs a full-warehouse clumping risk index field; S106, based on the overall warehouse clumping risk index field, controls the temperature and humidity of the warehouse.

[0022] In one implementation, the temperature and humidity acquisition nodes are divided into a first type of node and a second type of node; the first type of node is a temperature and humidity sensor attached to the surface of the maltodextrin packaging, used to collect the surface temperature and relative humidity of the material; the second type of node is a temperature and humidity sensor deployed in the storage space, used to collect the temperature and relative humidity inside the storage space; each first type of node has one second type of node within a preset range, forming a node pair.

[0023] In one implementation, the preset frequency is determined by technical personnel. First, actual physical parameters of the warehouse are collected, including the warehouse's length L, width W, and height H, to define the three-dimensional range of the material stacking area within the warehouse (excluding empty areas without materials, such as warehouse pillars, aisles, and equipment placement). The effective range of the grid construction is then determined to avoid invalid grids consuming computational resources. A uniform grid partitioning method is used to discretize the effective warehouse area into a three-dimensional spatial grid. The grid size is set according to the deployment density of temperature and humidity acquisition nodes, typically with a grid side length between 0.5-1m, determined by technical personnel. A mapping relationship between grid coordinates and the actual warehouse location is established, recording the actual physical location corresponding to each grid point. Simultaneously, the coordinates of the temperature and humidity acquisition nodes and the moisture potential difference network nodes are associated to ensure that subsequent data can be accurately assigned to the corresponding grid points.

[0024] In one implementation, constructing a full-warehouse clumping risk index field based on the temperature and humidity spatiotemporal dataset, moisture migration parameters, and equivalent moisture increment includes: constructing a three-dimensional spatial grid based on the storage space; assigning the temperature and humidity data and moisture migration parameters collected by each second-type node to the three-dimensional spatial grid, and supplementing grid points without node coverage using a preset coverage algorithm (inverse distance weighted interpolation) to obtain a basic environmental temperature and humidity field and a moisture migration field covering the entire warehouse; mapping the equivalent moisture increment corresponding to each first-type node to the three-dimensional spatial grid, and performing equivalent moisture increment diffusion based on a preset infiltration diffusion distance to obtain an infiltration risk field; and weightedly fusing the basic environmental temperature and humidity field, the moisture migration field, and the infiltration risk field to obtain a full-warehouse clumping risk index field.

[0025] In one implementation, for each second-type node, the collected temperature and humidity data, as well as the corresponding moisture migration parameters, are extracted. Based on the three-dimensional coordinates of the second-type node, the grid point with the best matching coordinates in the three-dimensional spatial grid is found. The temperature and humidity data and moisture migration parameters of the node are directly assigned to the grid point, completing the mapping of discrete node data to the grid. The above operation is repeated to complete the data assignment for all second-type nodes. At this time, some nodes in the grid have complete temperature and humidity data and moisture migration parameters. The unassigned grid points are blank, and the blank grid points are supplemented by an inverse distance weighted interpolation algorithm as a preset coverage algorithm.

[0026] In one implementation, a permeability risk field is constructed, and the equivalent moisture increment corresponding to each node is extracted. The first type of node is attached to the surface of the maltodextrin packaging. Its three-dimensional coordinates are used as the initial diffusion origin of the equivalent moisture increment. The grid point in the three-dimensional space grid that best matches the coordinates of this origin is found, and the equivalent moisture increment is directly assigned to this grid point as the diffusion starting point. The preset penetration diffusion distance D is set to 1-2m, and a penetration diffusion coefficient is introduced. The value ranges from 0.8 to 1.2. Diffusion occurs from each diffusion initiation point to surrounding grid points. The equivalent moisture increment at each grid point is calculated, and the following definition is provided. For grid points Spatial distance from the diffusion initiation point, when When greater than or equal to D A value of 0 indicates that the diffusion range is exceeded, and therefore no penetration effect is considered. When the diffusion formula is less than D, the diffusion formula is: ,in For nodes The corresponding equivalent increase in moisture content, Let e ​​be the equivalent water increment corresponding to the diffusion initiation point, and e be the natural constant. For grid points The spatial distance from the diffusion starting point; if the diffusion ranges of multiple diffusion starting points overlap, the equivalent moisture increment of the overlapping area is the sum of the values ​​diffused from each starting point to that area; after completing the diffusion calculation for all starting points, the penetration risk field covering the entire effective area of ​​the warehouse is obtained.

[0027] In one implementation, the temperature and humidity of each grid point are normalized to the maximum and minimum values ​​of hazardous temperature and humidity based on preset hazardous maximum and minimum values, mapping them to the [0,1] interval. These preset hazardous maximum and minimum values ​​are determined by technical personnel. Similarly, the moisture migration field and the infiltration risk field are normalized to the minimum and maximum values ​​corresponding to these parameters, mapping them to the [0,1] interval. Temperature and humidity weights are defined as 0.3, moisture migration weight as 0.5, and infiltration weight as 0.2. For each grid point, the formula is used... Calculate the risk index for each grid point; where The weighting coefficients are the basic environmental temperature and humidity fields. These are the weighting coefficients for the water migration field. These are the weighting coefficients for the penetration risk field. For the third in the three-dimensional spatial mesh The temperature and humidity of the grid, For the third in the three-dimensional spatial mesh Mesh moisture migration parameters For the third in the three-dimensional spatial mesh The equivalent moisture increment of the grid; here , , The corresponding values ​​are all normalized values.

[0028] In one implementation, a severe threshold (range 0.7-0.85) is defined, requiring immediate intervention when the risk index exceeds this value; a warning threshold (range 0.5-0.7) is defined, requiring attention when the risk index exceeds this value. Rule 1: If a grid has a risk index greater than the severe threshold, and the distance from the grid center to the nearest logistics channel is less than 1.5 meters, then the nearest ventilation device affecting that grid is found, and that device is activated at high airflow mode for 15 minutes. If the grid remains at severe risk, the operating time is extended or adjacent devices are activated. Rule 2: If at least n adjacent grids have risk indices all exceeding the warning threshold, then the device with the most coverage in that area is identified, and the device is activated. Lower the temperature setpoint by 1-2°C or the humidity setpoint by 5-10%. If the area also includes a logistics channel, activate local ventilation simultaneously. Rule 3: If a grid risk index exceeds the severe threshold, but the location is a pre-marked dead zone, activate the dehumidifier if there is a reserved dehumidifier interface in the dead zone. If there is no fixed equipment, trigger an alarm to prompt manual placement of a mobile dehumidifier or adjustment of the storage location. Rule 4: If the average risk index of the entire warehouse exceeds the preset danger threshold, which is determined by technical personnel and ranges from 0.55 to 0.75, adjust the setpoints of all main air conditioners and dehumidifier units towards a safe direction, and simultaneously perform localized treatment according to rules 1-3.

[0029] In one implementation, the preset frequency is determined by technical personnel. Temperature and humidity data from each sampling point are synchronously collected at the preset frequency to obtain spatiotemporal data. Based on this data, a moisture potential difference network is constructed, and moisture migration parameters are calculated. Combined with the barrier properties of maltodextrin packaging, the equivalent moisture increment is determined. This leads to the construction of a whole-warehouse clumping risk index field, which is used to regulate the storage temperature and humidity. This enables comprehensive synchronous monitoring of the entire warehouse's temperature and humidity, precise quantification of the moisture migration state between materials and the environment, and consideration of the impact of packaging barrier performance on clumping risk. It can accurately locate the clumping risk in different areas of the entire warehouse and perform targeted temperature and humidity regulation, effectively solving the problems of limited data collection range, inaccurate risk assessment, and coarse regulation in traditional monitoring methods. This reduces maltodextrin clumping, ensures storage quality, reduces raw material waste and economic losses, and optimizes storage energy consumption, meeting the quality control needs of large-scale, refined maltodextrin storage.

[0030] In one embodiment, constructing a moisture potential difference network based on the spatiotemporal data of temperature and humidity corresponding to all sampling points includes: A data acquisition network was constructed based on the locations corresponding to all second-type nodes in the temperature and humidity spatiotemporal dataset. Through formula group The environmental moisture potential corresponding to each second-type sampling point was calculated. and the material moisture potential corresponding to each first-type sampling point ; Where ln is the natural logarithm, RH is the relative humidity inside the warehouse, and T is the temperature inside the warehouse. This is a specific gas constant for water vapor. Here, ERH represents the surface temperature of the material, and ERH represents the relative humidity of the material surface. For each node pair, the difference between the corresponding environmental moisture potential and the material moisture potential is calculated to obtain the moisture potential difference. An initial water potential difference network is constructed based on the water potential difference corresponding to the location of the second type of sampling point in all node pairs; Based on the data acquisition network, the initial water potential difference network is expanded to obtain the water potential difference network.

[0031] In one implementation, a data collection network is built based on the temperature and humidity spatiotemporal dataset deployed across the entire domain by the second type of nodes. This network can spatially anchor environmental monitoring points and material storage spaces, breaking the limitations of single-point monitoring and realizing the collection of moisture potential across the entire domain, in a gridded and spatiotemporally linked manner. This aligns with the actual scenario of three-dimensional moisture absorption and dampness in the storage of powder materials such as maltodextrin.

[0032] In one implementation method, the traditional temperature and humidity difference comparison is abandoned, and the two-way moisture potential difference of the environment and materials is used as the core indicator: the moisture potential difference is the essential driving force for water vapor migration and material moisture absorption and agglomeration. It can accurately capture the underlying mechanism of water vapor penetration and moisture absorption diffusion, and predict the risk of agglomeration and mold growth earlier than conventional temperature and humidity monitoring.

[0033] In one embodiment, the water potential difference network is obtained by expanding the initial water potential difference network based on the data acquisition network, including: The initial data update network is obtained by updating the nodes of the initial water potential difference network as known sample points in the data acquisition network. For each unpaired first-class node in the initial data update network, if there are known sample points within a preset distance of the node, the moisture potential difference of the node is estimated by weighting the moisture potential difference of the known sample points within the preset distance in inverse square proportion. If there is no known sample point within the preset distance of the node, the water potential difference of the node is calculated by using the nearest known sample point of the node through a multiple linear regression model. The water potential difference network is obtained by determining the water potential difference of each node in the initial data update network.

[0034] In one implementation, the preset distance is determined by technicians. The data acquisition network is updated by using known sample points. Based on the existing precise matching nodes, the effective local data is spread to the entire network. This solves the pain point that traditional methods rely solely on actual measurement points and gap areas where there is no data, and cannot model corners, middle layers of material piles, and storage edges. This allows the entire moisture potential difference network to be spatially continuous and without breaks.

[0035] In one implementation, two sets of supplementary calculation logic are used for layered processing: when there are samples nearby, the inverse square distance weighted estimation is used, which conforms to the physical laws of spatial water vapor diffusion and gradual change of potential energy, resulting in natural local interpolation with small errors; when there are no samples nearby, a multiple linear regression model is used to ensure that reliable values ​​can still be output for remote and sparse points, without losing nodes or empty data.

[0036] In one implementation, the moisture potential difference is a field quantity that changes gradually with spatial distance. The distance-inverse weighting fits the characteristics of near-field water vapor migration and potential energy decay. The regression model can be linked with multi-dimensional features such as surrounding temperature and humidity and spatial location to extrapolate, which is more scientific than simple mean filling and fixed assignment. It has strong anti-interference ability and the estimated value is closer to the actual moisture potential distribution in the warehouse.

[0037] In one implementation, the entire update, interpolation, and model recalculation process is clear and the rules are fixed. The preset distance, weighting coefficient, and regression features are configurable and reproducible. The generated complete moisture potential difference network can be directly used as the input base for risk heat maps, clumping prediction models, and closed-loop control of ventilation and dehumidification, making it easy to embed into automated warehouse management and control systems.

[0038] In one embodiment, based on a moisture potential difference network, the moisture migration parameters obtained by calculating the moisture migration state between maltodextrin material and the storage environment include: For each node in the moisture potential difference network, the moisture migration state between the maltodextrin material and the storage environment is determined based on the moisture potential difference of that node; the migration state includes the hygroscopic state, the desorption state, and the equilibrium state. If a node's migration state is not in equilibrium, then the formula is used. Calculate the rate of water migration; in, This represents the water migration rate value. This represents the water potential difference value. The baseline migration rate value, This is a temperature correction factor; Through formula Calculate the cumulative migration amount; in, To update the cumulative migration volume, This represents the historical cumulative migration volume. When greater than 0 =1, When less than 0 -1, When equal to 0 =0, Preset frequency; The water migration parameters are obtained by determining the water migration direction, water migration rate, and cumulative migration amount at each node.

[0039] In one implementation, when the moisture potential difference is greater than 0, it is in a hygroscopic state; when the moisture potential difference is less than 0, it is in a desorption state; and when the moisture potential difference is equal to 0, it is in an equilibrium state.

[0040] In one implementation, the reference migration rate values ​​for the hygroscopic and desorption states are pre-calibrated experimentally and stored in a parameter library; the temperature correction coefficient is calculated using the formula... =1+β×(Tsurf-25), where β is the temperature sensitivity coefficient, ranging from 0.02 to 0.05, determined experimentally; Tsurf is determined by the surface temperature of the material, and only the value is calculated.

[0041] In one implementation, the three core states of moisture absorption, desorption, and equilibrium are determined node by node by relying on the global moisture potential difference network, abandoning the traditional method of crudely judging moisture by relying solely on temperature and humidity thresholds; and focusing on the hydrophilic and moisture-absorbing, clumping nature of maltodextrin powder, the direction of water vapor migration is defined from the source of the moisture potential driving force.

[0042] In one implementation, the cumulative migration amount is calculated using an iterative formula with directional signs to distinguish between positive moisture absorption accumulation, reverse desorption accumulation, and equilibrium zero accumulation. This approach can capture the instantaneous migration speed in real time and also accumulate effects over a long period of time. It can accurately predict the hidden risks of slow moisture absorption and gradual agglomeration, thus making up for the shortcomings of instantaneous monitoring in not being able to detect long-term hidden dangers.

[0043] In one embodiment, obtaining the barrier characteristic parameters of the maltodextrin packaging and determining the equivalent moisture increment based on the barrier characteristic parameters includes: The barrier properties of maltodextrin packaging were determined in advance through experiments. The barrier properties included water vapor permeability P, surface area of ​​a single package S, and mass of a single package of maltodextrin m. For each node, calculate the absolute value of the temperature difference between the temperature inside the warehouse and the surface temperature of the material. Through formula Calculate the equivalent water increment corresponding to the first type of node in each node pair; in, The absolute value of the temperature difference. This is the equivalent temperature correction factor. This is the preset frequency.

[0044] In one implementation, the equivalent temperature correction factor is α = 1 + γ × (Tenv - 25), where γ is the temperature sensitivity coefficient, ranging from 0.01 to 0.03, and Tenv is the ambient temperature value.

[0045] In one implementation, the equivalent moisture increment of the finished product packaging for each node is calculated using a fixed formula, quantifying the hidden water ingress effect of slow and micro-permeation of the packaging into a statistically significant and cumulative value. This approach can identify both open and damaged packaging that quickly becomes damp and capture the cumulative risk of long-term micro-permeation of intact packaging.

[0046] Based on the same inventive concept, this invention also provides a maltodextrin storage environment monitoring system. See also Figure 2 , Figure 2 A framework diagram of a maltodextrin storage environment monitoring system provided in this embodiment of the invention includes: The temperature and humidity data acquisition module is used to synchronously acquire ambient temperature data and relative humidity data of each sampling point at a preset frequency to obtain the spatiotemporal temperature and humidity data corresponding to each sampling point. The moisture potential difference network construction module is used to construct a moisture potential difference network based on the spatiotemporal data of temperature and humidity corresponding to all sampling points. The moisture migration parameter determination module is used to calculate the moisture migration state between maltodextrin material and the storage environment based on the moisture potential difference network to obtain moisture migration parameters. The equivalent moisture increment determination module is used to obtain the barrier characteristic parameters of maltodextrin packaging and determine the equivalent moisture increment based on the barrier characteristic parameters. The module for constructing the overall blockage risk index field is used to construct the overall blockage risk index field based on the spatiotemporal dataset of temperature and humidity, moisture migration parameters, and equivalent moisture increment. The warehouse environment monitoring module is used to regulate the temperature and humidity of the warehouse based on the overall warehouse clumping risk index field.

[0047] The maltodextrin storage environment monitoring system provided by this invention synchronously collects temperature and humidity data from each sampling point at a preset frequency, constructs a moisture potential difference network and calculates moisture migration parameters, determines the equivalent moisture increment by combining packaging barrier characteristics, constructs a whole-warehouse clumping risk index field, and adjusts temperature and humidity accordingly. This achieves synchronous monitoring of temperature and humidity throughout the warehouse, precise quantification of moisture migration status, and takes into account the impact of packaging barrier performance. It accurately locates clumping risks in different areas and performs targeted adjustments, effectively solving problems such as limited monitoring range, inaccurate assessment, and extensive control in traditional systems. This reduces clumping, ensures quality, reduces waste and losses, optimizes energy consumption, and meets the quality control needs of large-scale and refined warehousing.

[0048] The foregoing has described one embodiment of the present invention in detail, but this content is merely a preferred embodiment and should not be considered as limiting the scope of the present invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the scope of the claims of this invention.

Claims

1. A method for monitoring the storage environment of maltodextrin, characterized in that, The warehouse is equipped with multiple temperature and humidity monitoring points, and the method includes: The ambient temperature and relative humidity data of each sampling point are collected synchronously at a preset frequency to obtain the spatiotemporal data of temperature and humidity for each sampling point. A moisture potential difference network is constructed based on the spatiotemporal data of temperature and humidity corresponding to all sampling points; Based on the moisture potential difference network, the moisture migration state between the maltodextrin material and the storage environment is calculated to obtain the moisture migration parameters. Obtain the barrier characteristic parameters of maltodextrin packaging, and determine the equivalent moisture increment based on the barrier characteristic parameters; Based on the aforementioned temperature and humidity spatiotemporal dataset, moisture migration parameters, and equivalent moisture increment, a full-warehouse clumping risk index field is constructed. Warehouse temperature and humidity are controlled based on the aforementioned full-warehouse clumping risk index field.

2. The method for monitoring the storage environment of maltodextrin according to claim 1, characterized in that, The temperature and humidity acquisition nodes are divided into a first type of node and a second type of node. The first type of node is a temperature and humidity sensor attached to the surface of the maltodextrin packaging, used to collect the surface temperature and relative humidity of the material. The second type of node is a temperature and humidity sensor deployed in the storage space, used to collect the temperature and relative humidity inside the storage space. Each first type of node has one second type of node within a preset range, forming a node pair.

3. The method for monitoring the storage environment of maltodextrin according to claim 2, characterized in that, A moisture potential difference network was constructed based on the spatiotemporal data of temperature and humidity corresponding to all sampling points, including: A data acquisition network is constructed for the locations corresponding to all second-type nodes in the aforementioned temperature and humidity spatiotemporal dataset; Through formula group The environmental moisture potential corresponding to each second-type sampling point was calculated. and the material moisture potential corresponding to each first-type sampling point ; Where ln is the natural logarithm, RH is the relative humidity inside the warehouse, and T is the temperature inside the warehouse. This is a specific gas constant for water vapor. Here, ERH represents the surface temperature of the material, and ERH represents the relative humidity of the material surface. For each node pair, the difference between the corresponding environmental moisture potential and the material moisture potential is calculated to obtain the moisture potential difference. An initial water potential difference network is constructed based on the water potential difference corresponding to the location of the second type of sampling point in all node pairs; Based on the data acquisition network, the initial water potential difference network is expanded to obtain a water potential difference network.

4. The method for monitoring the storage environment of maltodextrin according to claim 3, characterized in that, Based on the data acquisition network, the initial water potential difference network is expanded to obtain a water potential difference network including: The nodes in the initial water potential difference network are used as known sample points to update the nodes of the data acquisition network to obtain the initial data update network. For each unpaired first-type node in the initial data update network, if there are known sample points within a preset distance of the node, the moisture potential difference of the node is estimated by weighting the moisture potential difference of the known sample points within the preset distance in inverse square proportion. If there is no known sample point within the preset distance of the node, the water potential difference of the node is calculated by using the nearest known sample point of the node through a multiple linear regression model. The water potential difference network is obtained by determining the water potential difference of each node in the initial data update network.

5. The method for monitoring the storage environment of maltodextrin according to claim 4, characterized in that, Based on the aforementioned moisture potential difference network, the moisture migration parameters obtained by calculating the moisture migration state between the maltodextrin material and the storage environment include: For each node in the moisture potential difference network, the moisture migration state between the maltodextrin material and the storage environment is determined based on the moisture potential difference of that node; the migration state includes hygroscopic state, desorption state and equilibrium state. If a node's migration state is not in equilibrium, then the formula is used. Calculate the rate of water migration; in, This represents the water migration rate value. This represents the water potential difference value. The baseline migration rate value, This is a temperature correction factor; Through formula Calculate the cumulative migration amount; in, To update the cumulative migration volume, This represents the historical cumulative migration volume. When greater than 0 =1, When less than 0 -1, When equal to 0 =0, Preset frequency; The water migration parameters are obtained by determining the water migration direction, water migration rate, and cumulative migration amount at each node.

6. The method for monitoring the storage environment of maltodextrin according to claim 2, characterized in that, Obtaining the barrier characteristic parameters of maltodextrin packaging, and determining the equivalent moisture increment based on the barrier characteristic parameters, includes: The barrier properties of maltodextrin packaging were determined in advance through experiments. The barrier properties included water vapor permeability P, surface area of ​​a single package S, and mass of a single package of maltodextrin m. For each node, calculate the absolute value of the temperature difference between the temperature inside the chamber and the surface temperature of the material. Through formula Calculate the equivalent water increment corresponding to the first type of node in each node pair; in, The absolute value of the temperature difference. This is the equivalent temperature correction factor. This is the preset frequency.

7. The method for monitoring the storage environment of maltodextrin according to claim 2, characterized in that, Based on the aforementioned temperature and humidity spatiotemporal dataset, moisture migration parameters, and equivalent moisture increment, a full-warehouse clumping risk index field is constructed, including: A three-dimensional spatial grid is constructed based on the aforementioned warehouse space; The temperature and humidity data and moisture migration parameters collected by each second type of node are assigned to the three-dimensional spatial grid. For grid points that are not covered by nodes, a preset coverage algorithm is used to supplement them to obtain the basic environmental temperature and humidity field and moisture migration field covering the entire warehouse. The equivalent moisture increment corresponding to each first type node is mapped to the three-dimensional spatial grid, and the equivalent moisture increment is diffused based on the preset infiltration diffusion distance to obtain the infiltration risk field. The basic environmental temperature and humidity field, the moisture migration field, and the infiltration risk field are weighted and fused to obtain the overall warehouse clumping risk index field.

8. The method for monitoring the storage environment of maltodextrin according to claim 7, characterized in that, The weighted fusion of the basic environmental temperature and humidity field, the moisture migration field, and the permeation risk field yields the overall clumping risk index field, which includes: For each grid point, using the formula Calculate the risk index for each grid point; in The weighting coefficients are the basic environmental temperature and humidity fields. These are the weighting coefficients for the water migration field. These are the weighting coefficients for the penetration risk field. For the third in the three-dimensional spatial mesh The temperature and humidity of the grid, For the third in the three-dimensional spatial mesh Mesh moisture migration parameters For the third in the three-dimensional spatial mesh The equivalent moisture increment of the grid.

9. A maltodextrin storage environment monitoring system, characterized in that, The system includes: The temperature and humidity data acquisition module is used to synchronously acquire ambient temperature and relative humidity data at each sampling point at a preset frequency, so as to obtain the spatiotemporal temperature and humidity data corresponding to each sampling point. The moisture potential difference network construction module is used to construct a moisture potential difference network based on the spatiotemporal data of temperature and humidity corresponding to all sampling points. The moisture migration parameter determination module is used to calculate the moisture migration state between the maltodextrin material and the storage environment based on the moisture potential difference network, and obtain the moisture migration parameters. The equivalent moisture increment determination module is used to obtain the barrier characteristic parameters of maltodextrin packaging and determine the equivalent moisture increment based on the barrier characteristic parameters. The whole-warehouse clumping risk index field construction module is used to construct the whole-warehouse clumping risk index field based on the temperature and humidity spatiotemporal dataset, moisture migration parameters and equivalent moisture increment; The warehouse environment monitoring module is used to regulate the temperature and humidity of the warehouse based on the overall warehouse clumping risk index field.

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