An in-bin temperature management method, system, device, and medium

Abstract

The application discloses a kind of in-store temperature management method, system, equipment and medium, and the application belongs to wisdom warehousing technical field.The method comprises: collecting the multi-point grain temperature data in the three-dimensional space inside granary, constructs the digital twin model of granary temperature field;The monitoring point exceeding the safety threshold is marked as initial abnormal point;Extract all the initial abnormal point and its surrounding adjacent monitoring point in the preset range, aggregate to form one or more continuous geometric space area, as the target cooling area to be controlled temperature;Obtain the basic physical attribute parameter of warehousing goods;Calculate the theoretical cooling demand and theoretical action duration corresponding to the target cooling area;Drive variable frequency refrigeration equipment and three-axis partition air supply pipe network, according to the theoretical cooling demand and theoretical action duration, accurately deliver cool air in the target cooling area.This technical solution can realize the accurate cooling capacity matching of target cooling area, to achieve the effect of energy saving and precise temperature control.

Description

Technical Field

[0001] This application belongs to the field of smart warehousing technology, specifically relating to a method, system, equipment, and medium for managing temperature inside a warehouse. Background Technology

[0002] In the grain storage sector, maintaining grain quality hinges on precise temperature control. Currently, the industry's mainstream temperature control solutions fall into two main categories: one is the passive cooling method, which uses low-temperature fresh air to lower the grain temperature to below 10°C in winter, relying on the insulation structure of the storage unit to delay the temperature rise in summer. When the local grain temperature exceeds 15°C, a mobile high-power grain cooler is used for forced cooling. The other is the active temperature control method, which activates the air conditioning system to provide comprehensive cooling when the grain temperature exceeds a threshold.

[0003] However, the aforementioned existing technologies all have significant drawbacks in practical applications. First, passive cold storage methods heavily rely on manual operation, resulting in slow response times and extremely high energy consumption of mobile equipment, making them unsuitable for handling sudden high-temperature anomalies. Second, existing temperature control technologies are inefficient in their cold energy delivery. Specifically, when only localized areas experience overheating, the system still needs to cool the entire grain silo, leading to a significant waste of cold energy and hindering true on-demand cooling. Therefore, overcoming the drawbacks of existing temperature control methods—such as inconvenient operation, high energy consumption, and insufficient temperature control accuracy—while ensuring storage safety has become a pressing technical challenge in this field. Summary of the Invention

[0004] This application provides a method, system, equipment, and medium for warehouse temperature management, aiming to solve the problems of energy waste and temperature control lag caused by rigid zoning boundaries and extensive cold energy delivery in existing grain warehouse temperature control technologies. By introducing physical property parameters of stored goods, it achieves dynamic reconstruction and precise cold energy matching of targeted cooling areas, thereby achieving energy saving, consumption reduction, and precise temperature control.

[0005] In a first aspect, embodiments of this application provide a method for managing warehouse temperature, the method comprising: Collect multi-point grain temperature data in the three-dimensional space inside the grain warehouse to construct a digital twin model of the grain warehouse temperature field; In the digital twin model, the current temperature value of any monitoring point is compared with a preset grain temperature safety threshold, and monitoring points that exceed the safety threshold are marked as initial anomalies. Extract all the initial abnormal points and their adjacent monitoring points within a preset range, and aggregate them to form one or more continuous geometric spatial regions as targeted cooling areas to be controlled. Obtain the basic physical property parameters of the currently stored items, including at least the particle size distribution and bulk density of the stored items; Based on the particle size distribution and bulk density, calculate the theoretical cooling capacity requirement and theoretical operating time corresponding to the targeted cooling area; The variable frequency refrigeration equipment and the three-axis zoned air supply network are driven to precisely deliver cold air to the targeted cooling area according to the theoretical cooling capacity requirement and theoretical operating time.

[0006] Furthermore, the heat conduction prediction model is used to calculate the thermal migration trajectory of the abnormal temperature rise area within a preset time period in the future; Based on the migration trajectory of the hot zone, the boundary range of the targeted cooling area is dynamically adjusted so that the cooling area can be adaptively reconstructed as the shape of the hot zone changes.

[0007] Furthermore, the calculation of the theoretical cooling capacity requirement corresponding to the targeted cooling area includes: Calculate the contact thermal resistance between particles based on the particle size distribution of the stored goods; By combining the equivalent thermal conductivity of the stored goods and the contact thermal resistance, the actual thermal diffusivity inside the pile of stored goods is obtained. Based on the actual thermal diffusivity, calculate the minimum cooling load required to maintain the targeted cooling area at a safe temperature.

[0008] Furthermore, after the variable frequency refrigeration equipment and the three-axis zoned air supply network supply air are used for air supply, the following is also included: Real-time monitoring of the actual temperature drop curve within the targeted cooling area; Calculate the deviation rate between the actual cooling curve and the expected cooling curve predicted based on the theoretical cooling load; When the deviation rate exceeds a preset threshold, the attenuation control strategy adjustment process is triggered.

[0009] Furthermore, the attenuation control strategy adjustment process specifically includes: If the deviation rate is manifested as a cooling lag, it is determined that the stored goods have caking or small particle aggregation. In this case, the operating frequency of the compressor of the variable frequency refrigeration equipment is increased and the duration of a single air supply is extended. If the deviation rate indicates excessively rapid cooling, it is determined that the current stored items have high porosity or large particle size. Therefore, the output power of the variable frequency refrigeration equipment is reduced, and the duration of a single air supply is shortened to prevent local overcooling.

[0010] Furthermore, the method also includes: When the stored items are detected to be large-particle materials, reduce the opening of the electric air valves in the three-axis zoned air supply network and adopt a high-frequency intermittent air supply mode to avoid fine particles being lifted by the airflow or blocking the air duct. When the stored goods are detected to be small-particle materials, the opening of the electric air valve and the air jet speed are increased to enhance the penetration ability of cold air into the deep layers of the material pile.

[0011] Furthermore, the method also includes: Establish a mapping table between different categories of stored goods and the aforementioned basic physical attribute parameters; Before the temperature control operation begins, the system receives the current category identifier of the stored goods input by the user and automatically retrieves the corresponding particle size distribution and bulk density parameters from the mapping table.

[0012] Secondly, embodiments of this application provide a warehouse temperature management system, the system comprising: a data acquisition terminal, a control terminal, and a response terminal; The acquisition terminal includes multiple temperature sensors arranged in a matrix inside the grain warehouse, used to collect grain temperature data at multiple points in the three-dimensional space inside the grain warehouse, and transmit the grain temperature data to the control terminal. The control terminal includes a grain temperature management server and a three-axis zoned air supply network controller; The grain temperature management server is used to execute the warehouse temperature management method as described in claims 1-7; The three-axis zoned air supply network controller is used to parse the control commands and generate corresponding electric air valve opening signals and frequency conversion drive signals. The response end includes a smart variable frequency warehouse-specific air conditioning unit and a three-axis zoned air supply network; The intelligent variable frequency warehouse air conditioning unit is used to receive the variable frequency drive signal and adjust the compressor operating frequency and the fan speed. The three-axis zoned air supply network includes a main air supply pipe set along the length of the grain silo, a transverse branch pipe set along the width, and a longitudinal branch air supply pipe nested along the height. Each pipe is equipped with an electric air valve corresponding to the opening signal of the electric air valve, which is used to accurately deliver cold air to the targeted cooling area.

[0013] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0014] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0015] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0016] The technical solution provided in this application embodiment achieves the linkage monitoring of natural physiological and active training states through non-invasive dual-parameter acquisition. It accurately determines the type of Eustachian tube dysfunction by combining quantitative indicators, and obtains the opening coordination index through dynamic segmented analysis, taking into account both functional results and process evaluation. Furthermore, the individual baseline value model established based on historical data avoids individual bias of uniform judgment standards, improves monitoring accuracy, and can customize personalized training parameters according to the type of abnormality. It can also achieve real-time dynamic guidance of training by combining phase difference, and evaluate the training effect by measuring individual functional deviation. This improves the objectivity and accuracy of Eustachian tube function monitoring, and allows patients to conduct more accurate training according to prompts during home rehabilitation training, adapting to the multi-scenario use needs of clinical screening and home care. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of the warehouse temperature management method provided in Embodiment 1 of this application; Figure 2 This is a schematic diagram of the overall system structure provided in this embodiment; Figure 3 This is a schematic diagram of the horizontal and vertical partitioning structure of the three-axis partitioned air supply duct network provided in this embodiment; Figure 4 This is a schematic diagram of the vertical zoning and electric damper arrangement of the three-axis zoned air supply duct network provided in this embodiment; Figure 5 This is a schematic diagram of the spatial layout of the gridded, matrix-style grain temperature monitoring sensor network provided in this embodiment; Figure 6 This is a schematic diagram of the in-warehouse air supply and return duct layout provided in this embodiment; Figure 7 This is a schematic diagram of the internal temperature management system provided in Embodiment 2 of this application; Figure 8 This is a schematic diagram of the structure of the electronic device provided in Embodiment 3 of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0019] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0020] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0021] The following detailed description, in conjunction with the accompanying drawings, of the warehouse temperature management method, system, equipment, and medium provided in this application, through specific embodiments and application scenarios, will be provided in detail.

[0022] Example 1 Figure 1 This is a flowchart illustrating the warehouse temperature management method provided in Embodiment 1 of this application. Figure 1 As shown, the specific steps include the following: S11 collects multi-point grain temperature data in the three-dimensional space inside the grain warehouse to construct a digital twin model of the grain warehouse temperature field.

[0023] A grain warehouse is a closed storage facility used for long-term storage of agricultural products such as grains and oilseeds. It can be a room-type warehouse, a shallow circular warehouse, or a vertical silo.

[0024] Three-dimensional space refers to the three-dimensional spatial region inside a grain warehouse formed by three orthogonal directions: length, width, and depth of the grain pile. For example, it can be the full depth space from the surface to the bottom of the grain pile, or it can be a cube-shaped space enclosed horizontally, vertically, and vertically.

[0025] Multi-point grain temperature data refers to the temperature values ​​collected by sensors arranged in a matrix or grid pattern in three-dimensional space. For example, it can be data from one measuring point per cubic meter, or data from one layer at a height of 0.5 meters.

[0026] A digital twin is a three-dimensional virtual visualization model that is synchronized with and mapped to the real-time data of a physical grain warehouse. For example, it can be a real-time updated 3D graphical model or a remotely operable virtual grain warehouse model.

[0027] This solution acquires temperature signals periodically or in real-time via a sensor network and uploads them to the processing unit. The system uses a matrix sensor network to achieve synchronous multi-point temperature acquisition and data aggregation. Then, the multi-point temperature data undergoes spatial interpolation, 3D modeling, and real-time rendering. Using the acquired data as input, the system generates a digital twin model of the temperature field that is consistent with the physical grain silo through algorithms.

[0028] S12, in the digital twin model, the current temperature value of any monitoring point is compared with a preset grain temperature safety threshold, and the monitoring point that exceeds the safety threshold is marked as an initial abnormal point.

[0029] A monitoring point refers to a specific location in a three-dimensional grid layout where temperature acquisition tasks are assigned. For example, it could be a monitoring point inside a grain pile, or a monitoring point on the edge or corner of a grain pile.

[0030] The current temperature value refers to the real-time temperature value of the grain obtained by the monitoring point at the time of collection. For example, it could be 25℃, 18℃, or 16℃.

[0031] The preset grain temperature safety threshold refers to the upper limit of the safe storage temperature of grain that is set in advance. For example, it can be 15℃, 18℃, or 20℃.

[0032] Specifically, a digital twin model can be used to compare real-time temperature with safety thresholds. The system reads and compares temperatures point by point, automatically determining whether temperatures are exceeding the safety threshold. Monitoring points where temperatures exceed the safety threshold are identified; these could be localized overheating points in the center of the grain pile or points near the silo walls.

[0033] S13, extract all the initial abnormal points and their adjacent monitoring points within a preset range, and aggregate them to form one or more continuous geometric spatial regions as targeted cooling areas to be controlled.

[0034] The preset range refers to the spatial diffusion radius or number of diffusion layers centered on the anomaly point set by the system. For example, it can be an outward extension of 0.3 meters or an extension of one layer of sensor grid.

[0035] Adjacent monitoring points refer to temperature measurement points located within the spatial range surrounding the initial anomaly point. These can be adjacent measurement points in the vertical, horizontal, or three-dimensional space, or even neighboring measurement points.

[0036] This process can merge discrete anomalies with adjacent points into a continuous spatial block. The system merges connected over-temperature measurement points into a single, continuous three-dimensional region.

[0037] A geometric space region refers to a three-dimensional region enclosed by boundaries in three-dimensional space. For example, it can be a cuboid region or an irregular three-dimensional region.

[0038] Targeted cooling zones refer to specific three-dimensional areas that require priority and precise cooling. These could be high-temperature blocks in the center of a grain pile or localized areas that heat up rapidly.

[0039] S14, obtain the basic physical property parameters of the currently stored items, the basic physical property parameters including at least the particle size distribution and bulk density of the stored items.

[0040] Stored goods refer to grains or oil crops stored in warehouses, such as wheat, corn, or rice.

[0041] Basic physical property parameters refer to the core physical indicators that determine the heat conduction, ventilation, and cooling characteristics of grain.

[0042] Particle size distribution refers to the proportion of particle sizes in stored goods. For example, it could be the particle size distribution of wheat or corn.

[0043] Bulk density refers to the weight of grain per unit volume. For example, it could be 750 kg / m³ for wheat or 720 kg / m³ for corn.

[0044] Specifically, physical parameters can be read from local configuration, cloud mapping tables, or user input. The system automatically obtains the current grain particle size and density data through configuration or invocation.

[0045] S15. Based on the particle size distribution and bulk density, calculate the theoretical cooling capacity requirement and theoretical operating time corresponding to the targeted cooling area.

[0046] Theoretical cooling demand refers to the amount of cooling required to lower a target area from its current temperature to a safe temperature. This can be the cooling demand per hour or the total cooling demand.

[0047] The theoretical duration of action refers to the air supply operation time required to complete the cooling of the targeted area, which can be 30 minutes or 60 minutes.

[0048] In this solution, numerical calculations can be performed based on thermal balance, thermal conductivity, and area volume. The system uses particle size and density as inputs, combined with the space volume, to calculate the required cooling capacity and air supply time.

[0049] S16 drives the variable frequency refrigeration equipment and the three-axis zoned air supply network to accurately deliver cold air to the targeted cooling area according to the theoretical cooling capacity requirement and theoretical duration.

[0050] Variable frequency refrigeration equipment refers to a warehouse-specific air conditioning unit equipped with a variable frequency compressor and a variable frequency fan. For example, it can be a smart variable frequency warehouse air conditioning unit or a variable frequency grain cooler.

[0051] A three-axis zoned air supply network refers to an air supply duct system that can achieve independent three-dimensional zoned control in the horizontal, vertical, and longitudinal directions. For example, it can be a ground cage nested duct network with electric air valves, or an axial air supply duct network with partitions.

[0052] "Drive" refers to the grain temperature management system sending control commands to the refrigeration equipment and air valves to start and adjust their operating status. The system outputs signals to start the unit and adjust the air valve opening and equipment operating frequency.

[0053] Precise air delivery refers to directing airflow only to the target area, while keeping non-target areas closed or at low airflow. The system opens the corresponding area's air valves and closes the channels in other areas to achieve targeted and quantitative cooling.

[0054] The technical solution provided in this embodiment achieves full-area temperature perception through three-dimensional multi-point temperature measurement and digital twin model. It locates abnormal points by threshold comparison and aggregates them into targeted cooling areas. It accurately calculates the cooling capacity and duration by combining the physical properties of the grain, drives the frequency converter and the three-axis pipeline network for directional cooling, realizes precise control of grain storage temperature, avoids energy waste caused by full-area cooling, and improves grain storage safety and operational economy.

[0055] In one embodiment, optionally, a heat conduction prediction model is used to calculate the thermal migration trajectory of the abnormal temperature rise area within a future preset time period; Based on the migration trajectory of the hot zone, the boundary range of the targeted cooling area is dynamically adjusted so that the cooling area can be adaptively reconstructed as the shape of the hot zone changes.

[0056] A heat conduction prediction model refers to a temperature change prediction model built based on the thermal conductivity of grain, ambient temperature, and initial temperature field. For example, it can be a finite element heat conduction model or an empirical formula prediction model.

[0057] An abnormally hot area refers to a three-dimensional area where the rate of temperature increase is too fast or continues to exceed the standard. For example, it can be a local area with slow temperature rise or a high-temperature area with rapid diffusion.

[0058] The preset time period refers to the duration of temperature trend prediction set by the system, such as 2 hours, 4 hours, or 12 hours.

[0059] The migration trajectory of a hot zone refers to the path of the location movement and morphological changes of a high-temperature region over a future period of time. For example, it can be a diffusion from the center to the edge, or an extension from the surface to the depth.

[0060] This solution modifies the boundary coordinates of the targeted cooling area in real time based on the prediction results. The system automatically updates the area range according to the migration trajectory to maintain complete cooling coverage. The cooling area automatically adjusts its shape and size as the heat zone changes, always covering potentially high-temperature areas without manual intervention, and automatically matches the latest shape of the heat zone.

[0061] This technical solution introduces a heat conduction prediction model to predict temperature trends, calculates the migration trajectory of hot zones in advance, and dynamically adjusts the cooling area, transforming temperature control from a passive response to an active anticipatory approach. This prevents the expansion and spread of high-temperature areas, improves the timeliness and effectiveness of temperature control, and ensures the stability and safety of grain storage throughout the entire process.

[0062] In one embodiment, optionally, calculating the theoretical cooling capacity requirement corresponding to the targeted cooling area includes: Calculate the contact thermal resistance between particles based on the particle size distribution of the stored goods; By combining the equivalent thermal conductivity of the stored goods and the contact thermal resistance, the actual thermal diffusivity inside the pile of stored goods is obtained. Based on the actual thermal diffusivity, calculate the minimum cooling load required to maintain the targeted cooling area at a safe temperature.

[0063] Contact thermal resistance refers to the resistance to heat transfer at the interface between grain particles. The larger the particle size and the less contact there is, the higher the contact thermal resistance is usually.

[0064] Equivalent thermal conductivity refers to the overall thermal conductivity parameter of a grain pile as a homogeneous medium. For example, it can be the equivalent thermal conductivity of a wheat grain pile or a corn grain pile.

[0065] The actual thermal diffusivity refers to the parameter representing the true heat dissipation capacity of a grain pile after considering corrections for contact thermal resistance. This parameter better reflects the actual heat dissipation characteristics of stored grain.

[0066] Minimum cooling load refers to the minimum cooling power required to maintain the temperature of a zone within a safe range. This value provides a quantitative basis for energy-saving cooling.

[0067] This method can solve for thermal resistance, thermal conductivity, and thermal diffusivity one by one using the thermophysical property formulas, and finally obtain the minimum cooling capacity value.

[0068] This technical solution calculates the contact thermal resistance of particles based on particle size distribution and obtains the true thermal diffusivity by correcting it with the equivalent thermal conductivity. It then calculates the minimum cooling load to make the cooling demand more consistent with the actual heat transfer characteristics of the grain pile, avoiding excessive or insufficient cooling. This maximizes energy reduction and improves temperature control accuracy while ensuring the cooling effect.

[0069] In one embodiment, optionally, after the variable frequency refrigeration equipment and the three-axis zoned air supply network supply air, the method further includes: Real-time monitoring of the actual temperature drop curve within the targeted cooling area; Calculate the deviation rate between the actual cooling curve and the expected cooling curve predicted based on the theoretical cooling load; When the deviation rate exceeds a preset threshold, the attenuation control strategy adjustment process is triggered.

[0070] Real-time monitoring refers to continuously collecting temperature data in a target area at fixed time intervals and uploading it synchronously throughout the entire process of air supply and cooling. For example, temperature data can be collected every 10 seconds.

[0071] The actual cooling curve refers to the real monitoring curve formed by the continuous change of temperature in the target area with the air supply time during the cooling process. The curve fully reflects the actual cooling rate, cooling range, and cooling effect.

[0072] The expected cooling curve is the ideal temperature change curve predicted based on theoretical cooling load, theoretical duration of action, and thermophysical parameters of the grain pile. It represents the standard, expected cooling rate, and target.

[0073] Deviation rate refers to the ratio of the temperature difference between the actual cooling curve and the expected cooling curve at the same moment to the expected temperature. It is used to quantitatively determine whether the cooling meets expectations.

[0074] The preset threshold refers to the maximum allowable deviation limit set by the system in advance, such as 10%, 15%, or 20%.

[0075] Specifically, when the deviation rate exceeds a preset threshold, the attenuation control strategy adjustment process can be automatically initiated, entering the real-time parameter optimization stage.

[0076] This technical solution calculates the deviation rate by real-time monitoring of the actual cooling curve and comparing it with the expected curve. It promptly detects the difference between theoretical calculations and actual conditions on site. When the deviation exceeds the limit, it automatically triggers the adjustment process, realizing closed-loop control of the cooling process. This avoids cooling failure or overcooling due to changes in material state, and improves the robustness and temperature control stability of the system.

[0077] In one embodiment, optionally, the attenuation control strategy adjustment process specifically includes: If the deviation rate is manifested as a cooling lag, it is determined that the stored goods have caking or small particle aggregation. In this case, the operating frequency of the compressor of the variable frequency refrigeration equipment is increased and the duration of a single air supply is extended. If the deviation rate indicates excessively rapid cooling, it is determined that the current stored items have high porosity or large particle size. Therefore, the output power of the variable frequency refrigeration equipment is reduced, and the duration of a single air supply is shortened to prevent local overcooling.

[0078] Cooling lag refers to a situation where the actual temperature decreases significantly slower than the expected rate, resulting in insufficient cooling within the same time frame and low cooling efficiency.

[0079] Crusting refers to the phenomenon where grain grains clump together due to compression, moisture absorption, or long-term storage, significantly increasing ventilation resistance. It is commonly seen in materials that have been stored for a long time or have high moisture content.

[0080] Small particle size aggregation refers to the concentrated distribution of small grain particles in a local area. The small gaps between the particles result in high resistance to airflow penetration, making it difficult for cold air to penetrate quickly.

[0081] Rapid cooling refers to a temperature drop that occurs significantly faster than expected, resulting in an excessive temperature decrease within a short period and potentially causing localized overcooling.

[0082] High porosity refers to a state in which grain piles have large gaps between particles, a loose structure, and low airflow resistance. It is commonly found in small-diameter or uniform granular materials.

[0083] Localized overcooling refers to a temperature in a localized area that is below the safe storage limit, which may lead to condensation, freezing damage, or deterioration in the quality of grain.

[0084] This solution can automatically adjust operating parameters such as compressor frequency, equipment output power, and single air supply duration according to the type of deviation, so that the cooling returns to the expected curve.

[0085] This technical solution automatically judges the physical state of the grain based on the type of cooling deviation, and adjusts the frequency, power and air supply duration of the refrigeration equipment accordingly. It strengthens cooling for compacted and small-particle-size materials, and weakens cooling for high-porosity and large-particle-size materials, achieving adaptive and precise control, balancing cooling effect and grain safety, and further improving the system's intelligence and energy-saving level.

[0086] In one embodiment, optionally, the method further includes: When the stored items are detected to be large-particle materials, reduce the opening of the electric air valves in the three-axis zoned air supply network and adopt a high-frequency intermittent air supply mode to avoid fine particles being lifted by the airflow or blocking the air duct. When the stored goods are detected to be small-particle materials, the opening of the electric air valve and the air jet speed are increased to enhance the penetration ability of cold air into the deep layers of the material pile.

[0087] Small-particle-size materials refer to grains with small particle diameters and small pores, such as wheat, rice, and rapeseed.

[0088] A three-axis zoned air supply network refers to an air supply duct system that allows independent control of airflow and pressure in three directions: horizontal, vertical, and longitudinal. It includes a floor duct, air supply ducts, motorized dampers, and zone partitions.

[0089] An electric damper is an airflow control valve driven by a motor and whose opening degree can be continuously adjusted. It is used to precisely control the air volume, air pressure, and air delivery range of branch circuits.

[0090] High-frequency intermittent air supply refers to an air supply method that alternates between short-term air supply and short-term pauses, such as air supply for 5 minutes and pause for 3 minutes in a cycle.

[0091] High-speed airflow can cause fine particles to become suspended and move, leading to problems such as dust, particle stratification, and surface condensation. Accumulation of fine particles in pipe bends, joints, or at filter locations can reduce ventilation cross-sections and increase resistance.

[0092] Large-particle-size materials refer to grains with larger particle diameters, heavier weights, and less susceptibility to airflow, such as corn or soybeans.

[0093] Air jet velocity refers to the initial flow velocity of cold air ejected from the air outlet. The higher the velocity, the farther the penetration distance and the stronger the deep penetration capability.

[0094] Deep penetration refers to the cold air penetrating to the bottom or center of the grain pile, ensuring uniform cooling between the interior and the surface.

[0095] This technical solution adjusts the opening of the air valve and the air supply mode according to the differences in material particle size. Small-sized materials use small air valves for intermittent air supply to prevent dust and blockage, while large-sized materials use large air valves for high jet flow to enhance deep penetration. It adapts to the ventilation characteristics of different grains, protects the quality of stored goods, and improves the utilization rate of cold air and temperature control uniformity.

[0096] In one embodiment, optionally, the method further includes: Establish a mapping table between different categories of stored goods and the aforementioned basic physical attribute parameters; Before the temperature control operation begins, the system receives the current category identifier of the stored goods input by the user and automatically retrieves the corresponding particle size distribution and bulk density parameters from the mapping table.

[0097] A mapping table is a pre-established data table stored in the system database that records the one-to-one correspondence between different grain types and parameters such as particle size distribution and bulk density. It supports fast querying, matching, and retrieval.

[0098] The category of stored goods refers to the specific types of grain stored in the warehouse, such as wheat, corn, rice, soybeans, rapeseed, etc.

[0099] Basic physical property parameters refer to the key indicators that determine the thermal conductivity, ventilation, and cooling characteristics of grain piles, mainly including particle size distribution and bulk density.

[0100] Category identifiers are numbers, codes, or names used to uniquely distinguish different types of grains, such as wheat 01, corn 02, and rice 03.

[0101] This solution can obtain the current grain category information input or selected by the user through a local human-machine interface, a mini-program, or a remote client. Then, based on the received category identifier, it can automatically match and read the corresponding particle size distribution and bulk density parameters from the mapping table, eliminating the need for manual input.

[0102] This technical solution establishes a mapping table between grain types and physical parameters, enabling automatic parameter retrieval and configuration. This simplifies on-site operation procedures, reduces human input errors, improves system deployment and switching efficiency, adapts to multi-category grain rotation storage scenarios, and enhances the versatility and ease of use of the solution.

[0103] To enable those skilled in the art to better understand this solution, this application also provides a preferred embodiment.

[0104] This embodiment discloses a targeted temperature control system for grain storage, which mainly includes three parts: a three-axis zoned air supply network, a smart variable frequency storage-specific air conditioning unit, and a grain temperature management and control system; the grain temperature management and control system includes a gridded matrix sensor network, a digital twin modeling module, an AI control model, and a cloud management platform.

[0105] Figure 2 This is a schematic diagram of the overall system structure provided in this embodiment. Figure 2The overall architecture of the system of this invention is demonstrated, including the grain silo body, grain pile, gridded temperature sensors, three-axis zoned air supply network, variable frequency air conditioning unit, PLC controller, local operation terminal, cloud server, and mini-program terminal. The gridded temperature sensors are deployed inside the three-dimensional space of the grain pile to collect grain temperature data in real time; the three-axis zoned air supply network is buried under the grain pile at the bottom of the silo, responsible for horizontal, vertical, and three-dimensional zoned air supply; the intelligent variable frequency storage-specific air conditioning unit is connected to the three-axis zoned air supply network, providing adjustable cooling and airflow; the grain temperature management and control system establishes communication with the air conditioning unit and electric air valves through the PLC to realize data acquisition, model calculation, and control command issuance; the cloud server realizes data storage, remote monitoring, remote start / stop, and program upgrades; the mini-program terminal provides a simplified operation interface, supporting real-time viewing, remote control, and anomaly warning.

[0106] Figure 3 This is a schematic diagram of the horizontal and vertical partitioning structure of the three-axis partitioned air supply duct network provided in this embodiment. Figure 3 A plan view of the three-axis zoned air supply network is provided, demonstrating the horizontal and vertical zoning structure, which differs from traditional ground-cage air supply. Horizontal zoning: The main pipeline is arranged along the length of the grain silo, with branch electric dampers installed on the horizontal branch pipes, dividing the silo into multiple independent air supply zones along the width, each with independently controllable airflow. Vertical zoning: The branch pipes adopt a ground-cage nested branch air supply pipe structure, with partitions spaced along the axial direction of the ground cage, dividing the longitudinal direction into several segments. Each segment of the air supply pipe is equipped with an independent air supply damper, achieving precise vertical segmented air supply. This structure allows for precise distribution of cooling capacity along the two-dimensional zoning of horizontal and vertical directions, avoiding the waste of cooling capacity caused by traditional whole-silo air supply.

[0107] Figure 4 This is a schematic diagram of the vertical zoning and electric damper arrangement of the three-axis zoned air supply duct network provided in this embodiment. Figure 4 The cross-sectional structure of the three-axis zoned air supply network is shown, with a focus on the vertical zoned control method. Vertical zoning: Each horizontal and vertical branch air valve is an electric air valve, and its opening degree is calculated in real time and controlled in a closed loop by the grain temperature management system; by adjusting the opening degree of the electric air valve, the air pressure and air volume of each branch are precisely controlled to achieve vertical stratified cooling of the grain pile: the air volume is increased in the high-temperature layer and decreased or closed in the low-temperature layer; the three axes (horizontal, vertical, and vertical) are independently controllable, forming a matrix-type precision cooling network that can provide individual cooling to any three-dimensional sub-region, truly achieving targeted temperature control.

[0108] Figure 5This is a schematic diagram of the spatial layout of the gridded, matrix-style grain temperature monitoring sensor network provided in this embodiment. The sensors are evenly distributed in a three-dimensional matrix: they are arranged at equal intervals along the length, width, and depth directions, for example, a horizontal spacing of 2–3m, a vertical spacing of 2–3m, and a vertical spacing of 0.5–1m. The sensor network fully covers the grain pile, collecting grain temperature data from multiple points in three-dimensional space in real time and uploading it to the grain temperature management and control system. The system constructs a digital twin model of the grain silo based on the three-dimensional temperature data, which visualizes the temperature field distribution inside the silo in real time, identifies high-temperature points, and analyzes the temperature rise trend, providing a data foundation for targeted temperature control.

[0109] Figure 6 This is a schematic diagram of the air supply and return duct layout provided in this embodiment. The green ducts are air supply ducts, and the gray ducts are air outlet ducts, used to create circulating airflow within the grain pile and ensure that negative pressure does not form inside. The green air supply ducts connect to the three-axis zoned air supply network, precisely delivering the cold air generated by the variable frequency air conditioning unit to each targeted cooling area inside the grain pile. The gray air outlet ducts are located on the upper part or side wall of the grain pile, used to exhaust the hot air after heat exchange in the grain pile, creating a downward-supplying and upward-returning airflow circulation within the grain pile. By rationally matching the airflow through the supply and return ducts, the pressure inside the grain pile is maintained at a slightly positive or normal pressure, avoiding problems such as leakage, condensation, and grain surface dampness caused by negative pressure. The air outlet ducts can be equipped with dehumidifying fans, which start and stop in conjunction with the humidity inside the grain pile, taking into account both temperature and humidity control requirements.

[0110] This technical solution involves a grain temperature management and control system that collects real-time data from gridded sensors to construct a digital twin temperature field; identifies abnormally high-temperature areas and aggregates them into targeted cooling zones; calculates theoretical cooling capacity and air supply duration based on grain type, particle size distribution, and bulk density; drives intelligent variable frequency air conditioning units to output corresponding cooling capacity; and precisely delivers air to the targeted areas through a three-axis zoned air supply network; monitors the cooling curve in real time and dynamically corrects control parameters; uses an AI model to continuously learn and optimize control strategies; and enables remote monitoring and maintenance via a cloud platform. Ultimately, this solution achieves full network coverage, matrix monitoring, precise cooling, and targeted temperature control, ensuring that both the annual average temperature and the highest single-point temperature of the grain silo meet standards, thus achieving safe grain storage, energy conservation, and intelligent convenience.

[0111] Example 2 Figure 7 This is a schematic diagram of the internal temperature management system provided in Embodiment 2 of this application. Figure 7 As shown, the system includes: a data acquisition terminal 701, a control terminal 702, and a response terminal 703; The acquisition terminal 701 includes multiple temperature sensors arranged in a matrix inside the grain warehouse, used to collect grain temperature data at multiple points in the three-dimensional space inside the grain warehouse, and transmit the grain temperature data to the control terminal. The control terminal 702 includes a grain temperature management server and a three-axis zoned air supply network controller; The grain temperature management server is used to execute the warehouse temperature management method as described in the above embodiments. The three-axis zoned air supply network controller is used to parse the control commands and generate corresponding electric air valve opening signals and frequency conversion drive signals. The response terminal 703 includes a smart variable frequency warehouse-specific air conditioning unit and a three-axis zoned air supply network; The intelligent variable frequency warehouse air conditioning unit is used to receive the variable frequency drive signal and adjust the compressor operating frequency and the fan speed. The three-axis zoned air supply network includes a main air supply pipe set along the length of the grain silo, a transverse branch pipe set along the width, and a longitudinal branch air supply pipe nested along the height. Each pipe is equipped with an electric air valve corresponding to the opening signal of the electric air valve, which is used to accurately deliver cold air to the targeted cooling area.

[0112] The warehouse temperature management device in this application embodiment can be a system, or a component, integrated circuit, or chip in a terminal. The system can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network-attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application embodiment does not impose specific limitations.

[0113] The warehouse temperature management device in this embodiment can be a system with an operating system. This operating system can be Android, iOS, or other possible operating systems; this embodiment does not impose any specific limitations.

[0114] The warehouse temperature management device provided in this application embodiment can realize the various processes of the above embodiments, and will not be described again here to avoid repetition.

[0115] Example 3 like Figure 8As shown, this application embodiment also provides an electronic device 800, including a processor 801, a memory 802, and a program or instructions stored in the memory 802 and executable on the processor 801. When the program or instructions are executed by the processor 801, they implement the various processes of the above-described warehouse temperature management method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0116] It should be noted that the electronic devices in the embodiments of this application include mobile electronic devices and non-mobile electronic devices as described above.

[0117] Example 4 This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described warehouse temperature management method embodiments and achieve the same technical effect. To avoid repetition, these will not be described again here.

[0118] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0119] Example 5 This application also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described embodiments of the warehouse temperature management method and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0120] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system on a chip, etc.

[0121] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element. Furthermore, it should be noted that the scope of the methods and systems in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0122] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0123] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms fall within the scope of protection of this application.

[0124] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.

Claims

1. A method for managing temperature inside a warehouse, characterized in that, The method includes: Collect multi-point grain temperature data in the three-dimensional space inside the grain warehouse to construct a digital twin model of the grain warehouse temperature field; In the digital twin model, the current temperature value of any monitoring point is compared with a preset grain temperature safety threshold, and monitoring points that exceed the safety threshold are marked as initial anomalies. Extract all the initial abnormal points and their adjacent monitoring points within a preset range, and aggregate them to form one or more continuous geometric spatial regions as targeted cooling areas to be controlled. Obtain the basic physical property parameters of the currently stored items, including at least the particle size distribution and bulk density of the stored items; Based on the particle size distribution and bulk density, calculate the theoretical cooling capacity requirement and theoretical operating time corresponding to the targeted cooling area; The variable frequency refrigeration equipment and the three-axis zoned air supply network are driven to precisely deliver cold air to the targeted cooling area according to the theoretical cooling capacity requirement and theoretical operating time.

2. The method according to claim 1, characterized in that, The thermal migration trajectory of the abnormal temperature rise region within a preset time period is calculated using a heat conduction prediction model. Based on the migration trajectory of the hot zone, the boundary range of the targeted cooling area is dynamically adjusted so that the cooling area can be adaptively reconstructed as the shape of the hot zone changes.

3. The method according to claim 1, characterized in that, The calculation of the theoretical cooling capacity requirement corresponding to the targeted cooling area includes: Calculate the contact thermal resistance between particles based on the particle size distribution of the stored goods; By combining the equivalent thermal conductivity of the stored goods and the contact thermal resistance, the actual thermal diffusivity inside the pile of stored goods is obtained. Based on the actual thermal diffusivity, calculate the minimum cooling load required to maintain the targeted cooling area at a safe temperature.

4. The method according to claim 3, characterized in that, After the variable frequency refrigeration equipment and the three-axis zoned air supply network supply air are used for air supply, the following is also included: Real-time monitoring of the actual temperature drop curve within the targeted cooling area; Calculate the deviation rate between the actual cooling curve and the expected cooling curve predicted based on the theoretical cooling load; When the deviation rate exceeds a preset threshold, the attenuation control strategy adjustment process is triggered.

5. The method according to claim 4, characterized in that, The attenuation control strategy adjustment process specifically includes: If the deviation rate is manifested as a cooling lag, it is determined that the stored goods have a caking or large particle aggregation phenomenon. In this case, the operating frequency of the compressor of the variable frequency refrigeration equipment is increased and the duration of a single air supply is extended. If the deviation rate indicates excessively rapid cooling, it is determined that the current stored items have high porosity or small particle size. Therefore, the output power of the variable frequency refrigeration equipment is reduced, and the duration of a single air supply is shortened to prevent local overcooling.

6. The method according to claim 1, characterized in that, The method further includes: When the stored items are detected to be large-particle materials, reduce the opening of the electric air valves in the three-axis zoned air supply network and adopt a high-frequency intermittent air supply mode to avoid fine particles being lifted by the airflow or blocking the air duct. When the stored goods are detected to be small-particle materials, the opening of the electric air valve and the air jet speed are increased to enhance the penetration ability of cold air into the deep layers of the material pile.

7. The method according to claim 1, characterized in that, The method further includes: Establish a mapping table between different categories of stored goods and the aforementioned basic physical attribute parameters; Before the temperature control operation begins, the system receives the current category identifier of the stored goods input by the user and automatically retrieves the corresponding particle size distribution and bulk density parameters from the mapping table.

8. A warehouse temperature management system, characterized in that, The system includes: a data acquisition terminal, a control terminal, and a response terminal; The acquisition terminal includes multiple temperature sensors arranged in a matrix inside the grain warehouse, used to collect grain temperature data at multiple points in the three-dimensional space inside the grain warehouse, and transmit the grain temperature data to the control terminal. The control terminal includes a grain temperature management server and a three-axis zoned air supply network controller; The grain temperature management server is used to execute the warehouse temperature management method as described in claims 1-7; The three-axis zoned air supply network controller is used to parse the control commands and generate corresponding electric air valve opening signals and frequency conversion drive signals. The response end includes a smart variable frequency warehouse-specific air conditioning unit and a three-axis zoned air supply network; The intelligent variable frequency warehouse air conditioning unit is used to receive the variable frequency drive signal and adjust the compressor operating frequency and the fan speed. The three-axis zoned air supply network includes a main air supply pipe set along the length of the grain silo, a transverse branch pipe set along the width, and a longitudinal branch air supply pipe nested along the height. Each pipe is equipped with an electric air valve corresponding to the opening signal of the electric air valve, which is used to accurately deliver cold air to the targeted cooling area.

9. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the warehouse temperature management method as described in any one of claims 1-7.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the warehouse temperature management method as described in any one of claims 1-7.

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