Intelligent system for grain depot based on digital twinning

Abstract

The application discloses a grain depot intelligent system based on digital twinning, relates to the technical field of grain depots, and solves the problems of low visualization degree of current grain depot storage management mode, untimely acquisition of grain condition information, and difficult remote visual control, and comprises a model generation module, an intelligent analysis module, an intelligent early warning module, a grain condition display module and an intelligent control module, the model generation module is used for generating a grain depot twinning model corresponding to a physical grain depot, the intelligent analysis module is used for analyzing grain samples of the physical grain depot, the intelligent early warning module is used for intelligently early warning the storage condition of the grain in the physical grain depot, the grain condition display module is used for displaying the corresponding grain depot twinning model in the physical grain depot, and the intelligent control module is used for intelligently controlling the storage condition of the physical grain depot, so that the application can acquire grain condition information in time, visually manage the grain depot and realize remote visual control of the grain depot.

Description

Technical Field

[0001] This invention belongs to the field of intelligent grain depot management technology, specifically an intelligent grain depot system based on digital twins. Background Technology

[0002] Grain depots are specialized buildings used for storing grain. They mainly include warehouses, storage yards, and facilities for metering, conveying, stacking, cleaning, loading and unloading, ventilation, and drying, and are equipped with measuring, sampling, inspection, and testing equipment. Grain depots generally require sturdiness, durability, moisture resistance, heat insulation, ventilation, air protection, and protection against rodents and birds. Therefore, grain depots are a crucial infrastructure for ensuring food security, thereby significantly enhancing food safety.

[0003] While grain depots are currently equipped with appropriate control equipment, their daily management still relies on regular manual inspections of grain conditions and pest infestations to ensure the safety and quality of stored grain. Reports are typically submitted to the responsible person in the form of data collection reports or work logs. Based on this data and their experience, the responsible person makes judgments and decisions before issuing orders for ventilation control and pest control. This method of grain depot management suffers from problems such as low visibility, untimely acquisition of grain condition information, and difficulty in remote visual control.

[0004] Therefore, this invention proposes an intelligent grain depot system based on digital twins. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by providing an intelligent grain depot system based on digital twins.

[0006] The technical problem to be solved by this invention is:

[0007] How to achieve a high degree of visualization in grain depot management, timely acquisition of grain information, and remote visual control.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] The intelligent grain depot system based on digital twins includes a physical grain depot, a data acquisition module, a database, and a digital twin platform. The digital twin platform includes a model generation module, a sampling module, an intelligent analysis module, an intelligent early warning module, a grain condition display module, and an intelligent control module.

[0010] The database is used to store 3D structural diagrams of physical grain depots, and the model generation module is used to generate grain depot twin models corresponding to the physical grain depots and send the grain depot twin models to the grain condition display module.

[0011] The sampling module is used to sample the grain stored in the physical grain depot, and obtain multiple grain samples; the intelligent analysis module is used to analyze the grain samples in the physical grain depot, and obtain the management temperature range, management humidity range, management oxygen content range and management carbon dioxide content range of the grain samples in the physical grain depot, and send them to the intelligent early warning module.

[0012] The data acquisition module is used to collect real-time storage data of the physical grain depot and send the real-time storage data of the physical grain depot to the intelligent early warning module; the intelligent early warning module is used to provide intelligent early warning of the storage status of grain in the physical grain depot, and the intelligent early warning generates normal signals or various abnormal signals and sends them to the intelligent control module and the grain condition display module.

[0013] The grain condition display module is used to display the corresponding grain warehouse twin model in the physical grain warehouse according to different signals, and the intelligent control module is used to intelligently control the storage status of the physical grain warehouse.

[0014] Furthermore, the analysis process of the intelligent analysis module is as follows:

[0015] Step P101: Place a grain sample in a simulated grain warehouse and obtain the initial insect content per unit volume of the corresponding grain sample.

[0016] Step P102: If the initial insect content per unit volume is zero, then record the current real-time temperature and humidity values ​​of the simulated grain warehouse as suitable temperature nodes and suitable humidity nodes, respectively.

[0017] If the initial insect content per unit volume is not zero, the real-time temperature and humidity values ​​of the simulated grain warehouse will be reduced to maintain a fixed time.

[0018] Step P103, then obtain the real-time insect content per unit volume of the corresponding grain sample in the simulated grain warehouse;

[0019] If the real-time insect content per unit volume of the grain sample is zero, then the current real-time temperature and humidity values ​​of the simulated grain warehouse will be recorded as suitable temperature nodes and suitable humidity nodes, respectively.

[0020] If the real-time insect content per unit volume of the grain sample is not equal to zero, then the real-time insect content per unit volume is compared with the initial insect content per unit volume. If the real-time insect content per unit volume is greater than or equal to the initial insect content per unit volume, then no operation is performed. If the real-time insect content per unit volume is less than the initial insect content per unit volume, then the current real-time insect content per unit volume is replaced with the initial insect content per unit volume.

[0021] Step P104, and so on, further reduce the real-time temperature and humidity values ​​of the simulated grain warehouse, so that the real-time temperature and humidity values ​​of the simulated grain warehouse are maintained for a fixed duration, and repeat step P103 to obtain multiple sets of suitable temperature nodes and suitable humidity nodes.

[0022] Step P105: If there are multiple sets of suitable temperature nodes and suitable humidity nodes, traverse and compare the multiple sets of suitable temperature nodes to obtain the minimum and maximum values ​​among the suitable temperature nodes. Take the minimum value among the suitable temperature nodes as the left endpoint and the maximum value among the suitable temperature nodes as the right endpoint. Construct the first suitable temperature range of the grain sample based on the left endpoint and the right endpoint. Similarly, traverse and compare the multiple sets of suitable humidity nodes to obtain the minimum and maximum values ​​among the suitable humidity nodes. Take the minimum value among the suitable humidity nodes as the left endpoint and the maximum value among the suitable humidity nodes as the right endpoint. Construct the first suitable humidity range of the grain sample based on the left endpoint and the right endpoint.

[0023] If there is only one set of suitable temperature nodes and suitable humidity nodes, proceed to step P112.

[0024] Furthermore, the analysis process of the intelligent analysis module also includes:

[0025] Step P106: Place another grain sample in the simulated grain warehouse and obtain the initial amount of mold per unit volume of the corresponding grain sample.

[0026] Step P107: If the initial amount of mold per unit volume is zero, then record the current real-time temperature and humidity values ​​of the simulated grain warehouse as suitable temperature nodes and suitable humidity nodes, respectively.

[0027] If the initial amount of mold per unit volume is not zero, the real-time temperature and humidity values ​​of the simulated grain warehouse will be reduced to maintain a fixed time.

[0028] Step P108, then obtain the real-time mold content per unit volume of the corresponding grain sample in the simulated grain warehouse;

[0029] If the real-time mold content per unit volume of the grain sample is zero, then the current real-time temperature and humidity values ​​of the simulated grain warehouse will be recorded as suitable temperature nodes and suitable humidity nodes, respectively.

[0030] If the real-time mold content per unit volume of the grain sample is not equal to zero, then the real-time mold content per unit volume is compared with the initial mold content per unit volume. If the real-time mold content per unit volume is greater than or equal to the initial mold content per unit volume, then no operation is performed. If the real-time mold content per unit volume is less than the initial mold content per unit volume, then the current real-time mold content per unit volume is replaced with the initial mold content per unit volume.

[0031] Step P109, and so on, further reduce the real-time temperature and humidity values ​​of the simulated grain warehouse, so that the real-time temperature and humidity values ​​of the simulated grain warehouse are maintained for a fixed duration, and repeat step P108 to obtain multiple sets of suitable temperature nodes and suitable humidity nodes.

[0032] Step P110: If there are multiple sets of suitable temperature nodes and suitable humidity nodes, traverse and compare the multiple sets of suitable temperature nodes to obtain the minimum and maximum values ​​among the suitable temperature nodes. Take the minimum value among the suitable temperature nodes as the left endpoint and the maximum value among the suitable temperature nodes as the right endpoint. Construct the second suitable temperature range for the grain sample based on the left endpoint and the right endpoint. Similarly, traverse and compare the multiple sets of suitable humidity nodes to obtain the minimum and maximum values ​​among the suitable humidity nodes. Take the minimum value among the suitable humidity nodes as the left endpoint and the maximum value among the suitable humidity nodes as the right endpoint. Construct the second suitable temperature range and the second suitable humidity range for the grain sample based on the left endpoint and the right endpoint.

[0033] If there is only one set of suitable temperature nodes and suitable humidity nodes, proceed to step P112.

[0034] Furthermore, the analysis process of the intelligent analysis module also includes:

[0035] Step P111: Obtain the first suitable temperature range, the second suitable temperature range, the first suitable humidity range, and the second suitable humidity range. Take the intersection of the first suitable temperature range and the second suitable temperature range as the management temperature range of the grain sample. Similarly, take the intersection of the first suitable humidity range and the second suitable humidity range as the management humidity range of the grain sample.

[0036] Step P112: Compare the two sets of suitable temperature nodes, take the smaller value of the two sets of suitable temperature nodes as the left endpoint, and the larger value of the two sets of suitable temperature nodes as the right endpoint, and construct the management temperature range of the grain sample based on the left endpoint and the right endpoint. Similarly, compare the two sets of suitable humidity nodes, take the smaller value of the two sets of suitable humidity nodes as the left endpoint, and the larger value of the two sets of suitable humidity nodes as the right endpoint, and construct the management humidity range of the grain sample based on the left endpoint and the right endpoint.

[0037] Furthermore, the analysis process of the intelligent analysis module also includes:

[0038] Step P113: Place a grain sample in a simulated grain warehouse and obtain the initial insect content per unit volume of the corresponding grain sample.

[0039] Step P114: If the initial insect content per unit volume is zero, then record the current real-time oxygen content and real-time carbon dioxide content of the simulated grain warehouse as suitable oxygen content nodes and suitable carbon dioxide content nodes, respectively.

[0040] If the initial insect content per unit volume is not zero, reduce the real-time oxygen content in the simulated grain warehouse and increase the real-time carbon dioxide content in the simulated grain warehouse, so that the real-time oxygen content and real-time carbon dioxide content in the simulated grain warehouse are maintained at a fixed duration, and then proceed to the next step.

[0041] Step P115: Repeat steps P103-P112 to obtain the managed oxygen content range and managed carbon dioxide content range of the grain sample.

[0042] Furthermore, the real-time stored data includes the real-time temperature, real-time humidity, and real-time insect content per unit volume of the physical grain depot, as well as the oxygen and carbon dioxide content per unit volume within the physical grain depot.

[0043] Furthermore, the intelligent early warning process of the intelligent early warning module is as follows:

[0044] The system obtains real-time temperature, humidity, insect content per unit volume, and gas content per unit volume of different gases in a physical grain depot.

[0045] Then, the management temperature range, management humidity range, management oxygen content range, and management carbon dioxide content range of the corresponding grain samples in the physical grain depot were obtained.

[0046] If the real-time temperature of the grain depot falls within the managed temperature range, the real-time humidity falls within the managed humidity range, the oxygen content per unit volume falls within the managed oxygen content range, and the carbon dioxide content per unit volume falls within the managed carbon dioxide content range, then a normal signal is generated.

[0047] Furthermore, the intelligent early warning process of the intelligent early warning module also includes:

[0048] If the real-time temperature of the grain depot is not within the management temperature range, a low temperature signal is generated when the real-time temperature of the grain depot is lower than the left end of the management temperature range, and a high temperature signal is generated when the real-time temperature of the grain depot is higher than the right end of the management temperature range.

[0049] If the real-time humidity of the grain depot is not within the managed humidity range, a low humidity signal is generated when the real-time humidity of the grain depot is less than the left end of the managed humidity range, and a high humidity signal is generated when the real-time humidity of the grain depot is greater than the right end of the managed humidity range.

[0050] If the oxygen content per unit volume does not fall within the managed oxygen content range, a low oxygen signal is generated when the oxygen content per unit volume is less than the left end of the managed oxygen content range, and a high oxygen signal is generated when the oxygen content per unit volume is greater than the right end of the managed oxygen content range.

[0051] If the carbon dioxide content per unit volume does not fall within the managed carbon dioxide content range, a low carbon dioxide signal is generated when the carbon dioxide content per unit volume is less than the left endpoint of the managed carbon dioxide content range, and a high carbon dioxide signal is generated when the carbon dioxide content per unit volume is greater than the right endpoint of the managed carbon dioxide content range.

[0052] Furthermore, the intelligent control process of the intelligent control module is as follows:

[0053] If a signal indicating that the temperature is too high is received, a cooling command is generated and sent to the control box. The control box then controls the axial flow fan to operate according to the cooling command.

[0054] If a low temperature signal is received, a heating command is generated and sent to the control box. The control box then controls the heater to operate according to the heating command.

[0055] If a high humidity signal is received, a dehumidification command is generated and sent to the control box. The control box then controls the dehumidifier to operate according to the dehumidification command.

[0056] If a low humidity signal is received, a humidification command is generated and sent to the control box. The control box then controls the humidifier to operate according to the humidification command.

[0057] Furthermore, the intelligent control process of the intelligent control module also includes:

[0058] If a signal indicating high oxygen levels is received, a deoxygenation command is generated and sent to the control box. The control box then controls the deoxygenation device to operate based on the deoxygenation command.

[0059] If a low oxygen level signal is received, an oxygenation command is generated and sent to the control box. The control box then controls the oxygenator to operate according to the oxygenation command.

[0060] If a signal indicating excessive carbon dioxide levels is received, a decarbonization command is generated and sent to the control box. The control box then controls the carbon dioxide purifier to operate based on the decarbonization command.

[0061] If a low carbon dioxide signal is received, a carbon increase command is generated and sent to the control box. The control box then controls the carbon dioxide generator to operate according to the carbon increase command.

[0062] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0063] 1. This invention first samples the grain stored in the physical grain depot to obtain multiple grain samples, and then analyzes the grain samples in the physical grain depot to obtain the management temperature range, management humidity range, management oxygen content range and management carbon dioxide content range of the grain samples in the physical grain depot, so as to realize intelligent and timely updating of grain storage data.

[0064] 2. This invention collects real-time storage data from physical grain depots and uses this data to provide intelligent early warnings about the storage status of grain in the physical grain depots. The warnings generate normal signals or various abnormal signals. By combining intelligently updated data with real-time collected data, this invention can obtain grain condition information in a timely manner, thereby achieving accurate judgment of the grain depot situation.

[0065] 3. This invention generates a twin model of the physical grain depot through a 3D structural diagram, and then displays the corresponding twin model of the physical grain depot according to different signals, and intelligently controls the storage status within the physical grain depot. This invention realizes visualized management of grain depots under different conditions and enables remote visualized control of grain depots. Attached Figure Description

[0066] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0067] Figure 1 This is an overall system block diagram of the present invention;

[0068] Figure 2 This is a schematic diagram illustrating the working principle of the physical grain depot of the present invention.

[0069] Figure 3 This is a flowchart of the method of the present invention. Detailed Implementation

[0070] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] Example 1:

[0072] Please see Figure 1 and Figure 2 As shown, the technical solution provided by the present invention is: an intelligent grain depot system based on digital twins, including a physical grain depot, a data acquisition module, a database, and a digital twin platform;

[0073] In this embodiment, the physical grain depot is used to store grain. The physical grain depot can be a flat warehouse, a shallow round warehouse, or a vertical silo. In this example, a flat warehouse is preferred.

[0074] The database is used to store 3D structural diagrams of physical grain depots. These 3D diagrams are three-dimensional representations of the buildings, data acquisition equipment, and grain depot equipment within the physical grain depot. In specific implementation, the data acquisition equipment includes cameras, temperature sensors, humidity sensors, and gas sensors. The grain depot control equipment includes axial flow fans, heaters, humidifiers, dehumidifiers, deoxygenation devices, oxygenators, carbon dioxide purifiers, carbon dioxide generators, and a control box. The control box is used to control the start and stop of the axial flow fans, heaters, humidifiers, dehumidifiers, deoxygenation devices, oxygenators, carbon dioxide purifiers, and carbon dioxide generators.

[0075] In this embodiment, the digital twin platform includes a model generation module, a sampling module, an intelligent analysis module, an intelligent early warning module, a grain condition display module, and an intelligent control module;

[0076] Furthermore, the model generation module is used to use programming technology to drive the synchronous operation of the physical grain depot through variable binding relationships, and to realize the virtual-real mapping of the grain depot through data transmission technology, thereby obtaining the grain depot twin model corresponding to the physical grain depot, and sending the grain depot twin model to the grain condition display module.

[0077] In this embodiment, the specific steps for generating the grain depot twin model are as follows:

[0078] Step S1: Obtain the 3D structural diagram corresponding to the physical grain depot, and obtain a three-dimensional diagram of the buildings and equipment inside the physical grain depot;

[0079] It should be specifically explained that the virtual-physical mapping of grain depots includes static mapping and dynamic mapping. Static mapping of grain depots involves mapping the buildings and equipment of the physical grain depot to the twin grain depot, while dynamic mapping of grain depots involves mapping the changes in the grain depot equipment through dynamic control of commands to the twin grain depot.

[0080] Step S2: Calculate the grain depot twin model using the twin model relationship formula. The specific twin model relationship formula is as follows:

[0081] DVB=DVB ev +DVB pe ①;

[0082] In equation ①, DVB represents the set of all twin models, namely the grain depot twin model. ev DVB represents the entity model of environmental elements. pe Represents a twin model of a non-environmental entity;

[0083] DVB ev =DVB ar +DVB eq ②;

[0084] In formula ②, DVB arDVB represents a building twin model. eq Represents a twin model for device classes;

[0085] DVB eq =m*DVB cam +n*DVB sen +d*DVB fen +k*DVB cs ③;

[0086] In formula ③, DVB cam Represents a camera-type twin model, DVB sen This represents a sensor twin model, where m is the number of cameras, and DVB... fen DVB represents the twin model of an axial flow fan. cs This represents a dehumidifier twin model, where n is the number of sensors, d is the number of axial fans, and k is the number of dehumidifiers.

[0087] Step S3: Combining the twin model relationship formulas ①, ② and ③ with the grain depot twin model, and using 3D modeling software to perform geometric modeling and rendering of buildings and related equipment such as warehouses, cameras, and control boxes, the first twin grain depot is obtained.

[0088] It should be noted that the 3D modeling software can be 3DMAX, CEO, UG, etc. As a large-scale macro model, the grain depot involves many types of models and complex structures. The surface mesh of the first twin grain depot generally contains a huge number of triangular facets. These huge amounts of data will put a huge burden on network transmission and WebGL-based visualization, thus requiring lightweight processing of the first twin grain depot.

[0089] Step S4: Perform lightweighting on the first twin grain depot to obtain the second twin grain depot;

[0090] In this embodiment, step S4 includes the following sub-steps:

[0091] Step S401: Use 3DMAX software to delete the invisible faces, edges and vertices in the model of the first twin grain store, and merge the duplicate vertices and edges to obtain the lightweight FBX model.

[0092] Step S402: Import the FBX file corresponding to the FBX model into the Unity 3D engine platform to build the grain depot scene, and use view frustum culling and multi-level of detail technology to further perform real-time lightweighting during system operation to obtain the second twin grain depot.

[0093] Step S5: Establish a network connection between the physical grain depot and the second twin grain depot to realize the virtual-physical mapping of the grain depot and obtain the corresponding twin model of the physical grain depot.

[0094] As a further embodiment of the present invention, the data acquisition module is used to collect real-time storage data of the physical grain depot and send the real-time storage data of the physical grain depot to the intelligent early warning module in the digital twin platform.

[0095] In this example, the data acquisition module connects to the digital twin platform through a preset communication protocol, which may include Ethernet, Bluetooth, and Wi-Fi.

[0096] It should be specifically noted that the real-time stored data includes the real-time temperature, humidity, and insect content per unit volume of the physical grain depot, as well as the oxygen and carbon dioxide content per unit volume within the physical grain depot. In practice, the data acquisition module refers to the data acquisition equipment installed within the physical grain depot.

[0097] In this embodiment, the sampling module is used to sample the grain stored in the physical grain depot, obtaining multiple grain samples; the intelligent analysis module is used to analyze the grain samples in the physical grain depot, and the analysis process is as follows:

[0098] Step P101: Place a grain sample in a simulated grain warehouse and obtain the initial insect content per unit volume of the corresponding grain sample.

[0099] In reality, simulated grain warehouses have the same storage and control functions as physical grain warehouses, except that simulated grain warehouses are used for storing grain samples for experiments.

[0100] Step P102: If the initial insect content per unit volume is zero, then record the current real-time temperature and humidity values ​​of the simulated grain warehouse as suitable temperature nodes and suitable humidity nodes, respectively.

[0101] If the initial insect content per unit volume is not zero, the real-time temperature and humidity values ​​of the simulated grain warehouse will be reduced to maintain a fixed time.

[0102] It should be specifically noted that high temperature and high humidity can easily cause grain in physical grain warehouses to sprout, mold, and deteriorate, providing a suitable environment for pests to breed. Low temperature and low humidity help inhibit the development of mold and pests, but excessively low temperature may lead to a decline in the quality of grain in physical grain warehouses. Therefore, in the analysis, by continuously reducing the temperature and humidity, the grain in physical grain warehouses is kept at a suitable temperature and humidity to reduce the impact of mold and pests on grain quality. In this example, the temperature and humidity in the physical grain warehouse can be regulated by controlling the axial flow fan and dehumidifier. At the same time, a high oxygen environment promotes pest activity, while a low oxygen environment can inhibit pest activity. Therefore, in this example, a carbon dioxide generator can be used to increase the carbon dioxide concentration in the physical grain warehouse to suffocate pests.

[0103] Step P103, then obtain the real-time insect content per unit volume of the corresponding grain sample in the simulated grain warehouse;

[0104] If the real-time insect content per unit volume of the grain sample is zero, then the current real-time temperature and humidity values ​​of the simulated grain warehouse will be recorded as suitable temperature nodes and suitable humidity nodes, respectively.

[0105] If the real-time insect content per unit volume of the grain sample is not equal to zero, then the real-time insect content per unit volume is compared with the initial insect content per unit volume. If the real-time insect content per unit volume is greater than or equal to the initial insect content per unit volume, then no operation is performed. If the real-time insect content per unit volume is less than the initial insect content per unit volume, then the current real-time insect content per unit volume is replaced with the initial insect content per unit volume.

[0106] Step P104, and so on, further reduce the real-time temperature and humidity values ​​of the simulated grain warehouse, so that the real-time temperature and humidity values ​​of the simulated grain warehouse are maintained for a fixed duration, and repeat step P103 to obtain multiple sets of suitable temperature nodes and suitable humidity nodes.

[0107] Step P105: If there are multiple sets of suitable temperature nodes and suitable humidity nodes, traverse and compare the multiple sets of suitable temperature nodes to obtain the minimum and maximum values ​​among the suitable temperature nodes. Take the minimum value among the suitable temperature nodes as the left endpoint and the maximum value among the suitable temperature nodes as the right endpoint. Construct the first suitable temperature range of the grain sample based on the left endpoint and the right endpoint. Similarly, traverse and compare the multiple sets of suitable humidity nodes to obtain the minimum and maximum values ​​among the suitable humidity nodes. Take the minimum value among the suitable humidity nodes as the left endpoint and the maximum value among the suitable humidity nodes as the right endpoint. Construct the first suitable humidity range of the grain sample based on the left endpoint and the right endpoint.

[0108] If there is only one set of suitable temperature nodes and suitable humidity nodes, proceed to step P112;

[0109] Step P106: Place another grain sample in the simulated grain warehouse and obtain the initial amount of mold per unit volume of the corresponding grain sample.

[0110] Step P107: If the initial amount of mold per unit volume is zero, then record the current real-time temperature and humidity values ​​of the simulated grain warehouse as suitable temperature nodes and suitable humidity nodes, respectively.

[0111] If the initial amount of mold per unit volume is not zero, the real-time temperature and humidity values ​​of the simulated grain warehouse will be reduced to maintain a fixed time.

[0112] Step P108, then obtain the real-time mold content per unit volume of the corresponding grain sample in the simulated grain warehouse;

[0113] If the real-time mold content per unit volume of the grain sample is zero, then the current real-time temperature and humidity values ​​of the simulated grain warehouse will be recorded as suitable temperature nodes and suitable humidity nodes, respectively.

[0114] If the real-time mold content per unit volume of the grain sample is not equal to zero, then the real-time mold content per unit volume is compared with the initial mold content per unit volume. If the real-time mold content per unit volume is greater than or equal to the initial mold content per unit volume, then no operation is performed. If the real-time mold content per unit volume is less than the initial mold content per unit volume, then the current real-time mold content per unit volume is replaced with the initial mold content per unit volume.

[0115] Step P109, and so on, further reduce the real-time temperature and humidity values ​​of the simulated grain warehouse, so that the real-time temperature and humidity values ​​of the simulated grain warehouse are maintained for a fixed duration, and repeat step P108 to obtain multiple sets of suitable temperature nodes and suitable humidity nodes.

[0116] In practice, the lower limit of the storage temperature corresponding to the physical grain depot is taken as the endpoint, that is, the last group is when the real-time temperature value of the simulated grain depot drops to the lower limit of the storage temperature.

[0117] Step P110: If there are multiple sets of suitable temperature nodes and suitable humidity nodes, traverse and compare the multiple sets of suitable temperature nodes to obtain the minimum and maximum values ​​among the suitable temperature nodes. Take the minimum value among the suitable temperature nodes as the left endpoint and the maximum value among the suitable temperature nodes as the right endpoint. Construct the second suitable temperature range for the grain sample based on the left endpoint and the right endpoint. Similarly, traverse and compare the multiple sets of suitable humidity nodes to obtain the minimum and maximum values ​​among the suitable humidity nodes. Take the minimum value among the suitable humidity nodes as the left endpoint and the maximum value among the suitable humidity nodes as the right endpoint. Construct the second suitable temperature range and the second suitable humidity range for the grain sample based on the left endpoint and the right endpoint.

[0118] If there is only one set of suitable temperature nodes and suitable humidity nodes, proceed to step P112;

[0119] Step P111: Obtain the first suitable temperature range, the second suitable temperature range, the first suitable humidity range, and the second suitable humidity range. Take the intersection of the first suitable temperature range and the second suitable temperature range as the management temperature range of the grain sample. Similarly, take the intersection of the first suitable humidity range and the second suitable humidity range as the management humidity range of the grain sample.

[0120] Step P112: Compare the two sets of suitable temperature nodes, take the smaller value of the two sets of suitable temperature nodes as the left endpoint, and take the larger value of the two sets of suitable temperature nodes as the right endpoint, and construct the management temperature range of the grain sample based on the left endpoint and the right endpoint. Similarly, compare the two sets of suitable humidity nodes, take the smaller value of the two sets of suitable humidity nodes as the left endpoint, and take the larger value of the two sets of suitable humidity nodes as the right endpoint, and construct the management humidity range of the grain sample based on the left endpoint and the right endpoint.

[0121] Step P113: Place a grain sample in a simulated grain warehouse and obtain the initial insect content per unit volume of the corresponding grain sample.

[0122] Step P114: If the initial insect content per unit volume is zero, then record the current real-time oxygen content and real-time carbon dioxide content of the simulated grain warehouse as suitable oxygen content nodes and suitable carbon dioxide content nodes, respectively.

[0123] If the initial insect content per unit volume is not zero, reduce the real-time oxygen content in the simulated grain warehouse and increase the real-time carbon dioxide content in the simulated grain warehouse, so that the real-time oxygen content and real-time carbon dioxide content in the simulated grain warehouse are maintained at a fixed duration, and then proceed to the next step.

[0124] Step P115: Repeat steps P103-P112 to obtain the range of managed oxygen content and managed carbon dioxide content of the grain sample.

[0125] In practice, the endpoint is the lower limit of the oxygen content or the upper limit of the carbon dioxide content in the physical grain depot. That is, the last set is when the real-time oxygen content in the simulated grain depot drops to the lower limit of the oxygen content or the real-time carbon dioxide content in the simulated grain depot rises to the carbon dioxide content.

[0126] The intelligent analysis module sends the managed temperature range, managed humidity range, managed oxygen content range, and managed carbon dioxide content range of grain samples in the physical grain depot to the intelligent early warning module.

[0127] In this embodiment, the intelligent early warning module is used to provide intelligent early warning of the storage status of grain in the physical grain depot. The specific steps of the intelligent early warning are as follows:

[0128] Step Q1: Obtain the real-time temperature, humidity, insect content per unit volume, and gas content per unit volume of different gases in the physical grain depot.

[0129] Step Q2, then obtain the management temperature range, management humidity range, management oxygen content range and management carbon dioxide content range of the corresponding grain samples in the physical grain depot;

[0130] Step Q3: If the real-time temperature of the grain depot is within the managed temperature range, the real-time humidity of the grain depot is within the managed humidity range, the oxygen content per unit volume is within the managed oxygen content range, and the carbon dioxide content per unit volume is within the managed carbon dioxide content range, then a normal signal is generated.

[0131] Step Q4: If the real-time temperature of the grain depot is not within the management temperature range, a low temperature signal is generated when the real-time temperature of the grain depot is lower than the left end of the management temperature range, and a high temperature signal is generated when the real-time temperature of the grain depot is higher than the right end of the management temperature range.

[0132] If the real-time humidity of the grain depot is not within the managed humidity range, a low humidity signal is generated when the real-time humidity of the grain depot is less than the left end of the managed humidity range, and a high humidity signal is generated when the real-time humidity of the grain depot is greater than the right end of the managed humidity range.

[0133] If the oxygen content per unit volume does not fall within the managed oxygen content range, a low oxygen signal is generated when the oxygen content per unit volume is less than the left end of the managed oxygen content range, and a high oxygen signal is generated when the oxygen content per unit volume is greater than the right end of the managed oxygen content range.

[0134] If the carbon dioxide content per unit volume does not fall within the managed carbon dioxide content range, a low carbon dioxide signal is generated when the carbon dioxide content per unit volume is less than the left end of the managed carbon dioxide content range, and a high carbon dioxide signal is generated when the carbon dioxide content per unit volume is greater than the right end of the managed carbon dioxide content range.

[0135] In reality, signals of low temperature may be generated simultaneously with signals of low oxygen levels. At the same time, signals of low temperature, low humidity, and high oxygen levels may also be generated simultaneously.

[0136] The intelligent early warning module sends normal signals or various abnormal signals to the intelligent control module and the grain condition display module.

[0137] In this embodiment, the grain condition display module is used to display the corresponding grain depot twin model in the physical grain depot according to different signals. The display process is as follows:

[0138] If a normal signal is received, the physical grain depot's corresponding grain depot twin model will be displayed in green.

[0139] If a signal indicating excessively high or low temperature is received, the corresponding twin model of the physical grain depot will be displayed in yellow.

[0140] If a high humidity signal or a low humidity signal is received, the corresponding grain depot twin model will be displayed in blue.

[0141] If a signal indicating high or low oxygen levels is received, the corresponding twin model of the physical grain depot will be displayed in purple.

[0142] If a signal indicating high or low carbon dioxide levels is received, the corresponding twin model of the physical grain depot will be displayed in red.

[0143] Specifically, the intelligent control module is used to intelligently control the storage status within the physical grain depot. The intelligent control process is as follows:

[0144] If a signal indicating that the temperature is too high is received, a cooling command is generated and sent to the control box. The control box then controls the axial flow fan to operate according to the cooling command.

[0145] If a low temperature signal is received, a heating command is generated and sent to the control box. The control box then controls the heater to operate according to the heating command.

[0146] If a high humidity signal is received, a dehumidification command is generated and sent to the control box. The control box then controls the dehumidifier to operate according to the dehumidification command.

[0147] If a low humidity signal is received, a humidification command is generated and sent to the control box. The control box then controls the humidifier to operate according to the humidification command.

[0148] If a signal indicating high oxygen levels is received, a deoxygenation command is generated and sent to the control box. The control box then controls the deoxygenation device to operate based on the deoxygenation command.

[0149] If a low oxygen level signal is received, an oxygenation command is generated and sent to the control box. The control box then controls the oxygenator to operate according to the oxygenation command.

[0150] If a signal indicating excessive carbon dioxide levels is received, a decarbonization command is generated and sent to the control box. The control box then controls the carbon dioxide purifier to operate based on the decarbonization command.

[0151] If a low carbon dioxide signal is received, a carbon increase command is generated and sent to the control box. The control box then controls the carbon dioxide generator to operate according to the carbon increase command.

[0152] In practice, such as Figure 2 The diagram shows the workflow of the second twin grain depot (grain depot twin model) controlling the physical grain depot. The network connection between the physical grain depot and the second twin grain depot (grain depot twin model) is the connection between the human-computer interaction layer and the virtual action layer, as well as the connection between the virtual action layer and the physical equipment layer. The human-computer interaction layer includes the user interface and the second twin grain depot, the virtual action layer includes the script parsing unit and the 3D action unit, and the physical equipment layer includes axial flow fans, dehumidifiers, and control boxes, etc.

[0153] First, a control request is initiated on the user interface according to the grain depot operation requirements, triggering the control script to transmit the target action data to the second twin grain depot. Then, after the control script is parsed in the virtual action layer, the control command is sent to the axial flow fan, dehumidifier and other automatic equipment in the physical grain depot through the communication protocol, so that they can complete the corresponding ventilation and dehumidification switching actions. At the same time, the control box feeds back the equipment status signal to the three-dimensional action unit, and the equipment status data drives the second twin grain depot to complete the switching actions synchronously.

[0154] In this application, if a corresponding calculation formula appears, the above calculation formula is a dimensionless calculation. The weighting coefficient, proportional coefficient and other coefficients in the formula are set to quantify each parameter to obtain a result value. The size of the weighting coefficient and proportional coefficient is only required to not affect the proportional relationship between the parameter and the result value.

[0155] Example 2;

[0156] Another concept based on the same invention, such as Figure 3 As shown in the figure, this embodiment proposes an intelligent grain depot method based on digital twins, the specific method is as follows:

[0157] Step S100: Sample the grain stored in the physical grain depot to obtain multiple grain samples;

[0158] Step S200: Analyze the grain samples in the physical grain depot to obtain the management temperature range, management humidity range, management oxygen content range, and management carbon dioxide content range of the grain samples in the physical grain depot.

[0159] Step S300: Collect real-time storage data of physical grain depots, and combine the real-time storage data to provide intelligent early warning of the storage status of grain in physical grain depots, generating normal signals or various abnormal signals.

[0160] Step S400: Obtain the grain warehouse twin model corresponding to the physical grain warehouse based on the 3D structure diagram;

[0161] Step S500: Display the corresponding twin model of the physical grain depot based on different signals, and intelligently control the storage status within the physical grain depot.

[0162] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A digital twin-based intelligent grain depot system, comprising a physical grain depot, a data acquisition module, a database, and a digital twin platform, characterized in that: The digital twin platform includes a model generation module, a sampling module, an intelligent analysis module, an intelligent early warning module, a grain condition display module, and an intelligent control module; The database is used to store 3D structural diagrams of physical grain depots, and the model generation module is used to generate grain depot twin models corresponding to the physical grain depots and send the grain depot twin models to the grain condition display module. The sampling module is used to sample the grain stored in the physical grain depot, and obtain multiple grain samples; the intelligent analysis module is used to analyze the grain samples in the physical grain depot, and obtain the management temperature range, management humidity range, management oxygen content range and management carbon dioxide content range of the grain samples in the physical grain depot, and send them to the intelligent early warning module. The data acquisition module is used to collect real-time storage data of the physical grain depot and send the real-time storage data of the physical grain depot to the intelligent early warning module; the intelligent early warning module is used to provide intelligent early warning of the storage status of grain in the physical grain depot, and the intelligent early warning generates normal signals or various abnormal signals and sends them to the intelligent control module and the grain condition display module. The grain condition display module is used to display the corresponding grain warehouse twin model in the physical grain warehouse according to different signals, and the intelligent control module is used to intelligently control the storage status of the physical grain warehouse.

2. The intelligent grain depot system based on digital twins according to claim 1, characterized in that, The analysis process of the intelligent analysis module is as follows: Step P101: Place a grain sample in a simulated grain warehouse and obtain the initial insect content per unit volume of the corresponding grain sample. Step P102: If the initial insect content per unit volume is zero, then record the current real-time temperature and humidity values ​​of the simulated grain warehouse as suitable temperature nodes and suitable humidity nodes, respectively. If the initial insect content per unit volume is not zero, the real-time temperature and humidity values ​​of the simulated grain warehouse will be reduced to maintain a fixed time. Step P103, then obtain the real-time insect content per unit volume of the corresponding grain sample in the simulated grain warehouse; If the real-time insect content per unit volume of the grain sample is zero, then the current real-time temperature and humidity values ​​of the simulated grain warehouse will be recorded as suitable temperature nodes and suitable humidity nodes, respectively. If the real-time insect content per unit volume of the grain sample is not equal to zero, then the real-time insect content per unit volume is compared with the initial insect content per unit volume. If the real-time insect content per unit volume is greater than or equal to the initial insect content per unit volume, then no operation is performed. If the real-time insect content per unit volume is less than the initial insect content per unit volume, then the current real-time insect content per unit volume is replaced with the initial insect content per unit volume. Step P104, and so on, further reduce the real-time temperature and humidity values ​​of the simulated grain warehouse, so that the real-time temperature and humidity values ​​of the simulated grain warehouse are maintained for a fixed duration, and repeat step P103 to obtain multiple sets of suitable temperature nodes and suitable humidity nodes. Step P105: If there are multiple sets of suitable temperature nodes and suitable humidity nodes, traverse and compare the multiple sets of suitable temperature nodes to obtain the minimum and maximum values ​​among the suitable temperature nodes. Take the minimum value among the suitable temperature nodes as the left endpoint and the maximum value among the suitable temperature nodes as the right endpoint. Construct the first suitable temperature range of the grain sample based on the left endpoint and the right endpoint. Similarly, traverse and compare the multiple sets of suitable humidity nodes to obtain the minimum and maximum values ​​among the suitable humidity nodes. Take the minimum value among the suitable humidity nodes as the left endpoint and the maximum value among the suitable humidity nodes as the right endpoint. Construct the first suitable humidity range of the grain sample based on the left endpoint and the right endpoint. If there is only one set of suitable temperature nodes and suitable humidity nodes, proceed to step P112.

3. The intelligent grain depot system based on digital twins according to claim 2, characterized in that, The analysis process of the intelligent analysis module also includes: Step P106: Place another grain sample in the simulated grain warehouse and obtain the initial amount of mold per unit volume of the corresponding grain sample. Step P107: If the initial amount of mold per unit volume is zero, then record the current real-time temperature and humidity values ​​of the simulated grain warehouse as suitable temperature nodes and suitable humidity nodes, respectively. If the initial amount of mold per unit volume is not zero, the real-time temperature and humidity values ​​of the simulated grain warehouse will be reduced to maintain a fixed time. Step P108, then obtain the real-time mold content per unit volume of the corresponding grain sample in the simulated grain warehouse; If the real-time mold content per unit volume of the grain sample is zero, then the current real-time temperature and humidity values ​​of the simulated grain warehouse will be recorded as suitable temperature nodes and suitable humidity nodes, respectively. If the real-time mold content per unit volume of the grain sample is not equal to zero, then the real-time mold content per unit volume is compared with the initial mold content per unit volume. If the real-time mold content per unit volume is greater than or equal to the initial mold content per unit volume, then no operation is performed. If the real-time mold content per unit volume is less than the initial mold content per unit volume, then the current real-time mold content per unit volume is replaced with the initial mold content per unit volume. Step P109, and so on, further reduce the real-time temperature and humidity values ​​of the simulated grain warehouse, so that the real-time temperature and humidity values ​​of the simulated grain warehouse are maintained for a fixed duration, and repeat step P108 to obtain multiple sets of suitable temperature nodes and suitable humidity nodes. Step P110: If there are multiple sets of suitable temperature nodes and suitable humidity nodes, traverse and compare the multiple sets of suitable temperature nodes to obtain the minimum and maximum values ​​among the suitable temperature nodes. Take the minimum value among the suitable temperature nodes as the left endpoint and the maximum value among the suitable temperature nodes as the right endpoint. Construct the second suitable temperature range for the grain sample based on the left endpoint and the right endpoint. Similarly, traverse and compare the multiple sets of suitable humidity nodes to obtain the minimum and maximum values ​​among the suitable humidity nodes. Take the minimum value among the suitable humidity nodes as the left endpoint and the maximum value among the suitable humidity nodes as the right endpoint. Construct the second suitable temperature range and the second suitable humidity range for the grain sample based on the left endpoint and the right endpoint. If there is only one set of suitable temperature nodes and suitable humidity nodes, proceed to step P112.

4. The intelligent grain depot system based on digital twins according to claim 3, characterized in that, The analysis process of the intelligent analysis module also includes: Step P111: Obtain the first suitable temperature range, the second suitable temperature range, the first suitable humidity range, and the second suitable humidity range. Take the intersection of the first suitable temperature range and the second suitable temperature range as the management temperature range of the grain sample. Similarly, take the intersection of the first suitable humidity range and the second suitable humidity range as the management humidity range of the grain sample. Step P112: Compare the two sets of suitable temperature nodes, take the smaller value of the two sets of suitable temperature nodes as the left endpoint, and the larger value of the two sets of suitable temperature nodes as the right endpoint, and construct the management temperature range of the grain sample based on the left endpoint and the right endpoint. Similarly, compare the two sets of suitable humidity nodes, take the smaller value of the two sets of suitable humidity nodes as the left endpoint, and the larger value of the two sets of suitable humidity nodes as the right endpoint, and construct the management humidity range of the grain sample based on the left endpoint and the right endpoint.

5. The intelligent grain depot system based on digital twins according to claim 4, characterized in that, The analysis process of the intelligent analysis module also includes: Step P113: Place a grain sample in a simulated grain warehouse and obtain the initial insect content per unit volume of the corresponding grain sample. Step P114: If the initial insect content per unit volume is zero, then record the current real-time oxygen content and real-time carbon dioxide content of the simulated grain warehouse as suitable oxygen content nodes and suitable carbon dioxide content nodes, respectively. If the initial insect content per unit volume is not zero, reduce the real-time oxygen content in the simulated grain warehouse and increase the real-time carbon dioxide content in the simulated grain warehouse, so that the real-time oxygen content and real-time carbon dioxide content in the simulated grain warehouse are maintained at a fixed duration, and then proceed to the next step. Step P115: Repeat steps P103-P112 to obtain the managed oxygen content range and managed carbon dioxide content range of the grain sample.

6. The intelligent grain depot system based on digital twins according to claim 1, characterized in that, The real-time stored data includes the real-time temperature, humidity, and insect content per unit volume of the physical grain depot, as well as the oxygen and carbon dioxide content per unit volume of the physical grain depot.

7. The intelligent grain depot system based on digital twins according to claim 6, characterized in that, The intelligent early warning process of the intelligent early warning module is as follows: The system obtains real-time temperature, humidity, insect content per unit volume, and gas content per unit volume of different gases in a physical grain depot. Then, the management temperature range, management humidity range, management oxygen content range, and management carbon dioxide content range of the corresponding grain samples in the physical grain depot were obtained. If the real-time temperature of the grain depot falls within the managed temperature range, the real-time humidity falls within the managed humidity range, the oxygen content per unit volume falls within the managed oxygen content range, and the carbon dioxide content per unit volume falls within the managed carbon dioxide content range, then a normal signal is generated.

8. The intelligent grain depot system based on digital twins according to claim 7, characterized in that, The intelligent early warning process of the intelligent early warning module also includes: If the real-time temperature of the grain depot is not within the management temperature range, a low temperature signal is generated when the real-time temperature of the grain depot is lower than the left end of the management temperature range, and a high temperature signal is generated when the real-time temperature of the grain depot is higher than the right end of the management temperature range. If the real-time humidity of the grain depot is not within the managed humidity range, a low humidity signal is generated when the real-time humidity of the grain depot is less than the left end of the managed humidity range, and a high humidity signal is generated when the real-time humidity of the grain depot is greater than the right end of the managed humidity range. If the oxygen content per unit volume does not fall within the managed oxygen content range, a low oxygen signal is generated when the oxygen content per unit volume is less than the left end of the managed oxygen content range, and a high oxygen signal is generated when the oxygen content per unit volume is greater than the right end of the managed oxygen content range. If the carbon dioxide content per unit volume does not fall within the managed carbon dioxide content range, a low carbon dioxide signal is generated when the carbon dioxide content per unit volume is less than the left endpoint of the managed carbon dioxide content range, and a high carbon dioxide signal is generated when the carbon dioxide content per unit volume is greater than the right endpoint of the managed carbon dioxide content range.

9. The intelligent grain depot system based on digital twins according to claim 8, characterized in that, The intelligent control process of the intelligent control module is as follows: If a signal indicating that the temperature is too high is received, a cooling command is generated and sent to the control box. The control box then controls the axial flow fan to operate according to the cooling command. If a low temperature signal is received, a heating command is generated and sent to the control box. The control box then controls the heater to operate according to the heating command. If a high humidity signal is received, a dehumidification command is generated and sent to the control box. The control box then controls the dehumidifier to operate according to the dehumidification command. If a low humidity signal is received, a humidification command is generated and sent to the control box. The control box then controls the humidifier to operate according to the humidification command.

10. The intelligent grain depot system based on digital twins according to claim 9, characterized in that, The intelligent control process of the intelligent control module also includes: If a signal indicating high oxygen levels is received, a deoxygenation command is generated and sent to the control box. The control box then controls the deoxygenation device to operate based on the deoxygenation command. If a low oxygen level signal is received, an oxygenation command is generated and sent to the control box. The control box then controls the oxygenator to operate according to the oxygenation command. If a signal indicating excessive carbon dioxide levels is received, a decarbonization command is generated and sent to the control box. The control box then controls the carbon dioxide purifier to operate based on the decarbonization command. If a low carbon dioxide signal is received, a carbon increase command is generated and sent to the control box. The control box then controls the carbon dioxide generator to operate according to the carbon increase command.

Images