Grain depot intelligent system based on digital twinning

Abstract

The invention discloses a grain depot intelligent system based on digital twinning, relates to the technical field of grain depots, solves the problems of low visualization degree, untimely grain condition information acquisition and difficult remote visualization control of a current grain depot storage management mode, 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 twinborn model corresponding to a physical grain depot, the intelligent analysis module is used for analyzing a grain sample of the physical grain depot, and the intelligent early warning module is used for performing intelligent early warning on the storage condition of grains in the physical grain depot. The grain condition display module is used for displaying the corresponding grain depot twinborn model in the physical grain depot, the intelligent control module is used for intelligently controlling the storage condition of the physical grain depot, the grain condition information can be obtained in time, the grain depot can be visually managed, and remote visual control over the grain depot is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent management of grain depots, and specifically relates to an intelligent grain depot system based on digital twins. Background Art

[0002] A grain depot is a special building for storing grain, which mainly includes warehouses, cargo yards, and facilities for measurement, transportation, stacking, cleaning, loading and unloading, ventilation, drying, etc. It is also equipped with measurement, sampling, inspection and testing, etc. Grain depots are generally required to be durable, moisture-proof, heat-insulated, ventilated, air-proof, and rat-proof. It can be seen that grain depots are important infrastructure for ensuring food security, thereby greatly improving food security.

[0003] Currently, grain depots are equipped with corresponding control equipment, but the daily management of grain depots still relies on manual inspections of grain conditions and pest conditions in the depot area to ensure the quality and safety of grain storage. Information is often collected and reported to the person in charge in the form of a report or work log. The person in charge makes judgments and decisions based on experience based on the reported data, and then issues orders to the executors to perform ventilation control and pest control and other operations. This type of grain depot storage management method has problems such as low visualization, untimely acquisition of grain information, and difficulty in remote visualization control.

[0004] To this end, the present invention proposes an intelligent grain warehouse system based on digital twins. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent grain storage system based on digital twins in view of the shortcomings of the prior art.

[0006] The technical problems to be solved by the present invention are: How to achieve high visualization of grain warehouse management, timely acquisition of grain information and remote visualization control.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: The grain depot intelligent system based on digital twins includes a physical grain depot, a data collection 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 status display module and an intelligent control module. The database is used to store the 3D structure diagram of the physical grain depot, and the model generation module is used to generate a grain depot twin model corresponding to the physical grain depot, and send the grain depot twin model to the grain status display module; The sampling module is used to sample the grain stored in the physical grain warehouse to obtain multiple grain samples; the intelligent analysis module is used to analyze the grain samples in the physical grain warehouse 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 warehouse 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 for 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 status display module; The grain status display module is used to display the corresponding grain depot twin model in the physical grain depot based on different signals, and the intelligent control module is used to intelligently control the storage status of the physical grain depot.

[0008] Furthermore, the analysis process of the intelligent analysis module is as follows: Step P101, placing a grain sample in a simulated granary to obtain an initial insect content per unit volume of the corresponding grain sample; Step P102, if the initial insect content per unit volume is zero, the current real-time temperature value and real-time humidity value of the simulated granary are recorded as the suitable temperature node and the suitable humidity node respectively; If the initial insect content per unit volume is not zero, the real-time temperature and humidity of the simulated granary are reduced so that the real-time temperature and humidity of the simulated granary are maintained for a fixed period of time; Step P103, then obtaining the real-time insect content per unit volume of the corresponding grain sample in the simulated granary; If the real-time insect content per unit volume of the grain sample is equal to zero, the current real-time temperature value and real-time humidity value of the simulated granary are recorded as the suitable temperature node and the suitable humidity node respectively; If the real-time insect content per unit volume of the grain sample is not equal to zero, the real-time insect content per unit volume is compared with the initial insect content per unit volume. When the real-time insect content per unit volume is greater than or equal to the initial insect content per unit volume, no operation is performed. When the real-time insect content per unit volume is less than the initial insect content per unit volume, 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 value and real-time humidity value of the simulated granary, so that the real-time temperature value and real-time humidity value of the simulated granary are maintained for a fixed time, and repeat step P103 to obtain multiple groups of suitable temperature nodes and suitable humidity nodes; Step P105, if there are multiple groups of suitable temperature nodes and suitable humidity nodes, traverse and compare the multiple groups of suitable temperature nodes to obtain the minimum and maximum values ​​in the suitable temperature nodes, use the minimum value in the suitable temperature nodes as the left endpoint, use the maximum value in the suitable temperature nodes as the right endpoint, and construct the first suitable temperature interval of the grain sample based on the left endpoint and the right endpoint. Similarly, traverse and compare the multiple groups of suitable humidity nodes to obtain the minimum and maximum values ​​in the suitable humidity nodes, use the minimum value in the suitable humidity nodes as the left endpoint, use the maximum value in the suitable humidity nodes as the right endpoint, and construct the first suitable humidity interval 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, go to step P112.

[0009] Furthermore, the analysis process of the intelligent analysis module also includes: Step P106, placing another grain sample in a simulated granary to obtain an initial unit volume moldy amount of the corresponding grain sample; Step P107, if the initial unit volume moldy amount is zero, then the current real-time temperature value and real-time humidity value of the simulated granary are recorded as the suitable temperature node and the suitable humidity node respectively; If the initial moldy amount per unit volume is not zero, the real-time temperature value and the real-time humidity value of the simulated granary are reduced so that the real-time temperature value and the real-time humidity value of the simulated granary are maintained for a fixed period of time; Step P108, then obtaining the real-time unit volume moldy amount of the corresponding grain sample in the simulated granary; If the real-time unit volume moldy amount of the grain sample is equal to zero, the current real-time temperature value and real-time humidity value of the simulated granary are recorded as the suitable temperature node and the suitable humidity node respectively; If the real-time moldy amount per unit volume of the grain sample is not equal to zero, the real-time moldy amount per unit volume is compared with the initial moldy amount per unit volume. When the real-time moldy amount per unit volume is greater than or equal to the initial moldy amount per unit volume, no operation is performed. When the real-time moldy amount per unit volume is less than the initial moldy amount per unit volume, the current real-time moldy amount per unit volume is replaced with the initial moldy amount per unit volume. Step P109, and so on, further reduce the real-time temperature value and real-time humidity value of the simulated granary, so that the real-time temperature value and real-time humidity value of the simulated granary are maintained for a fixed time, and repeat step P108 to obtain multiple groups of suitable temperature nodes and suitable humidity nodes; Step P110, if there are multiple groups of suitable temperature nodes and suitable humidity nodes, traverse and compare the multiple groups of suitable temperature nodes to obtain the minimum and maximum values ​​in the suitable temperature nodes, use the minimum value in the suitable temperature nodes as the left endpoint, use the maximum value in the suitable temperature nodes as the right endpoint, and construct the second suitable temperature interval of the grain sample based on the left endpoint and the right endpoint. Similarly, traverse and compare the multiple groups of suitable humidity nodes to obtain the minimum and maximum values ​​in the suitable humidity nodes, use the minimum value in the suitable humidity nodes as the left endpoint, use the maximum value in the suitable humidity nodes as the right endpoint, and construct the second suitable temperature interval and the second suitable humidity interval 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, go to step P112.

[0010] Furthermore, the analysis process of the intelligent analysis module also includes: Step P111, obtaining the first suitable temperature interval, the second suitable temperature interval, the first suitable humidity interval and the second suitable humidity interval, taking the intersection of the first suitable temperature interval and the second suitable temperature interval as the management temperature interval of the grain sample, and similarly, taking the intersection of the first suitable humidity interval and the second suitable humidity interval as the management humidity interval of the grain sample; Step P112, compare the sizes of the two groups of suitable temperature nodes, take the smaller value of the two groups of suitable temperature nodes as the left endpoint, take the larger value of the two groups 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 sizes of the two groups of suitable humidity nodes, take the smaller value of the two groups of suitable humidity nodes as the left endpoint, take the larger value of the two groups 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.

[0011] Furthermore, the analysis process of the intelligent analysis module also includes: Step P113, placing a grain sample in a simulated granary to obtain an initial insect content per unit volume of the corresponding grain sample; Step P114, if the initial insect content per unit volume is zero, the current real-time oxygen content and real-time carbon dioxide content of the simulated granary are recorded as the appropriate oxygen content node and the appropriate carbon dioxide content node respectively; If the initial insect content per unit volume is not zero, the real-time oxygen content in the simulated granary is reduced, and the real-time carbon dioxide content in the simulated granary is increased, so that the real-time oxygen content and the real-time carbon dioxide content in the simulated granary are maintained for a fixed period of time, and the next step is entered at the same time; Step P115, repeat steps P103-P112 to obtain the managed oxygen content range and the managed carbon dioxide content range of the grain sample.

[0012] Furthermore, the real-time stored data include the real-time temperature of the physical grain warehouse, the real-time humidity of the grain warehouse, the real-time insect content per unit volume of the grain warehouse, and the oxygen content per unit volume and the carbon dioxide content per unit volume in the physical grain warehouse.

[0013] Furthermore, the intelligent warning process of the intelligent warning module is as follows: Obtain the real-time temperature of the physical grain warehouse, the real-time humidity of the grain warehouse, the real-time insect content per unit volume of the grain warehouse, and the gas content per unit volume of different gases in the physical grain warehouse; Then, the management temperature range, management humidity range, management oxygen content range and management carbon dioxide content range of the grain samples corresponding to the physical grain warehouse are obtained; If the real-time temperature of the grain warehouse belongs to the managed temperature range, the real-time humidity of the grain warehouse belongs to the managed humidity range, the oxygen content per unit volume belongs to the managed oxygen content range, and the carbon dioxide content per unit volume belongs to the managed carbon dioxide content range, a normal signal is generated.

[0014] Furthermore, the intelligent warning process of the intelligent warning module also includes: If the real-time temperature of the grain depot does not belong to the management temperature range, when the real-time temperature of the grain depot is less than the left end point of the management temperature range, a low temperature signal is generated; when the real-time temperature of the grain depot is greater than the right end point of the management temperature range, a high temperature signal is generated; If the real-time humidity of the grain depot does not belong to the management humidity range, when the real-time humidity of the grain depot is less than the left end point of the management humidity range, a low humidity signal is generated; when the real-time humidity of the grain depot is greater than the right end point of the management humidity range, a high humidity signal is generated; If the oxygen content per unit volume does not belong to the managed oxygen content interval, when the oxygen content per unit volume is less than the left end point of the managed oxygen content interval, a low oxygen signal is generated; when the oxygen content per unit volume is greater than the right end point of the managed oxygen content interval, a high oxygen signal is generated; If the carbon dioxide content per unit volume does not belong to the managed carbon dioxide content range, when the carbon dioxide content per unit volume is less than the left end point of the managed carbon dioxide content range, a low carbon dioxide signal is generated; when the carbon dioxide content per unit volume is greater than the right end point of the managed carbon dioxide content range, a high carbon dioxide signal is generated.

[0015] Furthermore, the intelligent control process of the intelligent control module is specifically as follows: If a high temperature signal is received, a cooling command is generated and sent to the control box, and the control box controls the axial flow fan to work according to the cooling command; If a low temperature signal is received, a temperature increase instruction is generated and sent to the control box, and the control box controls the heater to work according to the temperature increase instruction; If a high humidity signal is received, a dehumidification command is generated and sent to the control box, and the control box controls the dehumidifier to work according to the dehumidification command; If a low humidity signal is received, a humidification command is generated and sent to the control box, and the control box controls the humidifier to work according to the humidification command.

[0016] Furthermore, the intelligent control process of the intelligent control module also includes: If a high oxygen signal is received, a deoxygenation instruction is generated and sent to the control box, and the control box controls the deoxygenation device to work according to the deoxygenation instruction; If a low oxygen signal is received, an oxygen increase instruction is generated and sent to the control box, and the control box controls the aerator to work according to the oxygen increase instruction; If a high carbon dioxide signal is received, a decarbonization instruction is generated and sent to the control box, and the control box controls the carbon dioxide purifier to work according to the decarbonization instruction; If a low carbon dioxide signal is received, a carbon increase instruction is generated and sent to the control box, and the control box controls the carbon dioxide generator to work according to the carbon increase instruction.

[0017] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. The present invention first samples the grain stored in the physical grain depot to obtain a plurality of 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, thereby realizing intelligent and timely updating of the grain depot storage data; 2. The present invention collects real-time storage data of physical grain depots, and combines the real-time storage data to perform intelligent early warning on the storage status of grain in the physical grain depot, and the early warning generates normal signals or various abnormal signals. Combined with the intelligently updated data and the real-time collected data, the present invention can obtain grain information in a timely manner, thereby realizing accurate judgment of the grain depot situation; 3. The present invention generates a twin model of a grain depot corresponding to a physical grain depot through a 3D structural diagram, and then displays the corresponding twin model of the grain depot in the physical grain depot according to different signals, and intelligently controls the storage status in the physical grain depot. The present invention realizes visual management of different situations of the grain depot and realizes remote visual control of the grain depot. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0019] Figure 1 is the overall system block diagram of the present invention; Figure 2 This is a working principle diagram of the physical grain depot of the present invention; Figure 3 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Embodiment 1: See also 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; In this embodiment, the physical grain warehouse is used to store grain. The physical grain warehouse can be a flat warehouse, a shallow round warehouse, or a vertical silo. In this example, the physical grain warehouse is preferably a flat warehouse; The database is used to store the 3D structure diagram of the physical grain warehouse. The 3D structure diagram is a three-dimensional diagram of the buildings, data acquisition equipment and grain warehouse equipment in the physical grain warehouse. In the specific implementation, the data acquisition equipment includes a camera, a temperature sensor, a humidity sensor and a gas sensor. The grain warehouse control equipment includes an axial flow fan, a heater, a humidifier, a dehumidifier, a deoxygenator, an aerator, a carbon dioxide purifier, a carbon dioxide generator and a control box. The control box is used to control the start and stop of the axial flow fan, the heater, the humidifier, the dehumidifier, the deoxygenator, the aerator, the carbon dioxide purifier and the carbon dioxide generator.

[0022] 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 status display module, and an intelligent control module; Furthermore, the model generation module is used to use programming technology to drive the twin grain depot to operate synchronously with the physical grain depot through variable binding relationship, and to realize the virtual-real mapping of the grain depot through data transmission technology, so as to obtain the twin model of the grain depot corresponding to the physical grain depot, and send the twin model of the grain depot to the grain situation display module.

[0023] In this embodiment, the specific steps of the grain depot twin model generation process are as follows: Step S1, obtaining a 3D structural diagram corresponding to a physical grain depot, and obtaining a stereoscopic diagram of buildings and grain depot equipment in the physical grain depot; It should be specifically explained that the virtual-real mapping of grain depots includes static mapping of grain depots and dynamic mapping of grain depots. Static mapping of grain depots is to map the buildings and equipment of the physical grain depot to the twin grain depot. Dynamic mapping of grain depots is to map the change process of the grain depot equipment to the twin grain depot through dynamic control of instructions. Step S2, the twin model of the grain depot is calculated by the twin model relationship formula, and the twin model relationship formula is as follows: DVB=DVB ev +DVB pe ①; In formula ①, DVB represents the set of all twin models, i.e., the twin model of grain depot, DVB ev Represents the entity model of environmental elements, DVB pe Represents a non-environmental entity twin model; DVB ev =DVB ar +DVB eq ②; DVB in formula ② ar Represents the building twin model, DVB eq Represents the device class twin model; DVB eq =m*DVB cam +n*DVB sen +d*DVB fen +k*DVB cs ③; DVB in formula ③ cam Represents the camera twin model, DVB sen represents the sensor twin model, m is the number of cameras, DVB fen Denotes the twin model of axial flow fan, DVB cs represents the dehumidifier twin model, n is the number of sensors, d is the number of axial fans, and k is the number of dehumidifiers; Step S3, combining the twin model relationship formulas ①, ② and ③ and the twin model of the grain depot, and using the 3D modeling software to geometrically model and render the warehouse, camera, control box and other buildings and related equipment to obtain the first twin grain depot; It should be specifically stated that the 3D modeling software can be 3DMAX, CEO, and UG. The grain depot, as a large-scale macro model, involves many types of models and complex structures. The surface mesh of the first twin grain depot initially established generally contains a huge number of triangular facets. These huge amounts of data will cause tremendous pressure on network transmission and WebGL-based visualization, and thus the first twin grain depot needs to be lightweighted. Step S4, performing lightweight processing on the first twin grain depot to obtain a second twin grain depot; In this embodiment, step S4 includes the following sub-steps: Step S401, using 3DMAX software to delete invisible faces, edges and vertices in the model of the first twin grain depot, and merge duplicate vertices and edges to obtain a lightweight FBX model; Step S402, importing the FBX file corresponding to the FBX model into the Unity 3D engine platform to build a grain depot scene, and using frustum culling and multi-level of detail technology to further perform real-time lightweighting during system operation to obtain a second twin grain depot; Step S5, establish a network connection between the physical grain depot and the second twin grain depot, realize the virtual-real mapping of the grain depot, and obtain a twin model of the physical grain depot corresponding to the grain depot.

[0024] As a further solution 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; In this example, the data acquisition module is connected to the digital twin platform through a preset communication protocol, which may include Ethernet, Bluetooth, and wireless network; It should be specifically explained that the real-time stored data include the real-time temperature of the physical grain warehouse, the real-time humidity of the grain warehouse, the real-time insect content per unit volume of the grain warehouse, and the oxygen content per unit volume and the carbon dioxide content per unit volume in the physical grain warehouse. In specific implementation, the data acquisition module is the data acquisition equipment installed in the physical grain warehouse.

[0025] In this embodiment, the sampling module is used to sample the grain stored in the physical grain warehouse to obtain multiple grain samples; the intelligent analysis module is used to analyze the grain samples in the physical grain warehouse, and the analysis process is as follows: Step P101, placing a grain sample in a simulated granary to obtain an initial insect content per unit volume of the corresponding grain sample; In reality, the simulated granary has the same storage and control functions as the physical granary, except that the simulated granary is used for storage tests of grain samples; Step P102, if the initial insect content per unit volume is zero, the current real-time temperature value and real-time humidity value of the simulated granary are recorded as the suitable temperature node and the suitable humidity node respectively; If the initial insect content per unit volume is not zero, the real-time temperature and humidity of the simulated granary are reduced so that the real-time temperature and humidity of the simulated granary are maintained for a fixed period of time; It should be specifically explained that high temperature and high humidity can easily cause the grain in the physical grain warehouse to germinate, mold, and deteriorate, providing an environment suitable for the breeding of pests. Low temperature and low humidity help to inhibit the development of mold and pests, but too low temperature may cause the quality of the grain in the physical grain warehouse to deteriorate. Therefore, during the analysis, the temperature and humidity are continuously reduced to keep the grain in the physical grain warehouse at a suitable temperature and humidity to reduce the impact of mold and pests on the quality of the grain. In this example, the temperature and humidity in the physical grain warehouse can be controlled by controlling the axial flow fan and the dehumidifier. At the same time, a high oxygen environment promotes pest activity, while a low oxygen environment can inhibit pest activity. In this example, the carbon dioxide concentration can be increased in the physical grain warehouse by a carbon dioxide generator to suffocate the pests. Step P103, then obtaining the real-time insect content per unit volume of the corresponding grain sample in the simulated granary; If the real-time insect content per unit volume of the grain sample is equal to zero, the current real-time temperature value and real-time humidity value of the simulated granary are recorded as the suitable temperature node and the suitable humidity node respectively; If the real-time insect content per unit volume of the grain sample is not equal to zero, the real-time insect content per unit volume is compared with the initial insect content per unit volume. When the real-time insect content per unit volume is greater than or equal to the initial insect content per unit volume, no operation is performed. When the real-time insect content per unit volume is less than the initial insect content per unit volume, 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 value and real-time humidity value of the simulated granary, so that the real-time temperature value and real-time humidity value of the simulated granary are maintained for a fixed time, and repeat step P103 to obtain multiple groups of suitable temperature nodes and suitable humidity nodes; Step P105, if there are multiple groups of suitable temperature nodes and suitable humidity nodes, traverse and compare the multiple groups of suitable temperature nodes to obtain the minimum and maximum values ​​in the suitable temperature nodes, use the minimum value in the suitable temperature nodes as the left endpoint, use the maximum value in the suitable temperature nodes as the right endpoint, and construct the first suitable temperature interval of the grain sample based on the left endpoint and the right endpoint. Similarly, traverse and compare the multiple groups of suitable humidity nodes to obtain the minimum and maximum values ​​in the suitable humidity nodes, use the minimum value in the suitable humidity nodes as the left endpoint, use the maximum value in the suitable humidity nodes as the right endpoint, and construct the first suitable humidity interval 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; Step P106, placing another grain sample in a simulated granary to obtain an initial unit volume moldy amount of the corresponding grain sample; Step P107, if the initial unit volume moldy amount is zero, then the current real-time temperature value and real-time humidity value of the simulated granary are recorded as the suitable temperature node and the suitable humidity node respectively; If the initial moldy amount per unit volume is not zero, the real-time temperature value and the real-time humidity value of the simulated granary are reduced so that the real-time temperature value and the real-time humidity value of the simulated granary are maintained for a fixed period of time; Step P108, then obtaining the real-time unit volume moldy amount of the corresponding grain sample in the simulated granary; If the real-time unit volume moldy amount of the grain sample is equal to zero, the current real-time temperature value and real-time humidity value of the simulated granary are recorded as the suitable temperature node and the suitable humidity node respectively; If the real-time moldy amount per unit volume of the grain sample is not equal to zero, the real-time moldy amount per unit volume is compared with the initial moldy amount per unit volume. When the real-time moldy amount per unit volume is greater than or equal to the initial moldy amount per unit volume, no operation is performed. When the real-time moldy amount per unit volume is less than the initial moldy amount per unit volume, the current real-time moldy amount per unit volume is replaced with the initial moldy amount per unit volume. Step P109, and so on, further reduce the real-time temperature value and real-time humidity value of the simulated granary, so that the real-time temperature value and real-time humidity value of the simulated granary are maintained for a fixed time, and repeat step P108 to obtain multiple groups of suitable temperature nodes and suitable humidity nodes; In the specific implementation, the lower limit of the storage temperature corresponding to the physical grain warehouse is taken as the end point, that is, the last group is when the real-time temperature value of the simulated grain warehouse drops to the lower limit of the storage temperature; Step P110, if there are multiple groups of suitable temperature nodes and suitable humidity nodes, traverse and compare the multiple groups of suitable temperature nodes to obtain the minimum and maximum values ​​in the suitable temperature nodes, use the minimum value in the suitable temperature nodes as the left endpoint, use the maximum value in the suitable temperature nodes as the right endpoint, and construct the second suitable temperature interval of the grain sample based on the left endpoint and the right endpoint. Similarly, traverse and compare the multiple groups of suitable humidity nodes to obtain the minimum and maximum values ​​in the suitable humidity nodes, use the minimum value in the suitable humidity nodes as the left endpoint, use the maximum value in the suitable humidity nodes as the right endpoint, and construct the second suitable temperature interval and the second suitable humidity interval 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; Step P111, obtaining the first suitable temperature interval, the second suitable temperature interval, the first suitable humidity interval and the second suitable humidity interval, taking the intersection of the first suitable temperature interval and the second suitable temperature interval as the management temperature interval of the grain sample, and similarly, taking the intersection of the first suitable humidity interval and the second suitable humidity interval as the management humidity interval of the grain sample; Step P112, compare the sizes of the two groups of suitable temperature nodes, take the smaller value of the two groups of suitable temperature nodes as the left endpoint, take the larger value of the two groups 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 sizes of the two groups of suitable humidity nodes, take the smaller value of the two groups of suitable humidity nodes as the left endpoint, take the larger value of the two groups 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; Step P113, placing a grain sample in a simulated granary to obtain an initial insect content per unit volume of the corresponding grain sample; Step P114, if the initial insect content per unit volume is zero, the current real-time oxygen content and real-time carbon dioxide content of the simulated granary are recorded as the appropriate oxygen content node and the appropriate carbon dioxide content node respectively; If the initial insect content per unit volume is not zero, the real-time oxygen content in the simulated granary is reduced, and the real-time carbon dioxide content in the simulated granary is increased, so that the real-time oxygen content and the real-time carbon dioxide content in the simulated granary are maintained for a fixed period of time, and the next step is entered at the same time; Step P115, repeating steps P103 to P112 to obtain the managed oxygen content interval and the managed carbon dioxide content interval of the grain sample; In specific implementation, the lower limit of the storage oxygen content or the upper limit of the storage carbon dioxide content corresponding to the physical grain depot is taken as the end point, that is, when the real-time oxygen content of the simulated grain depot drops to the lower limit of the storage oxygen content or the real-time carbon dioxide content of the simulated grain depot rises to the storage carbon dioxide content, it is the last group; The intelligent analysis module sends 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 warehouse to the intelligent early warning module.

[0026] In this embodiment, the intelligent early warning module is used to provide intelligent early warning for the storage status of grain in the physical grain depot. The intelligent early warning steps are as follows: Step Q1, obtaining the real-time temperature of the physical grain depot, the real-time humidity of the grain depot, the real-time insect content per unit volume of the grain depot, and the gas content per unit volume of different gases in the physical grain depot; Step Q2, then obtaining the management temperature range, management humidity range, management oxygen content range and management carbon dioxide content range of the grain sample corresponding to the physical grain warehouse; Step Q3: If the real-time temperature of the grain depot belongs to the managed temperature range, the real-time humidity of the grain depot belongs to the managed humidity range, the oxygen content per unit volume belongs to the managed oxygen content range, and the carbon dioxide content per unit volume belongs to the managed carbon dioxide content range, a normal signal is generated; Step Q4: if the real-time temperature of the grain depot does not belong to the management temperature interval, when the real-time temperature of the grain depot is less than the left end point of the management temperature interval, a low temperature signal is generated; when the real-time temperature of the grain depot is greater than the right end point of the management temperature interval, a high temperature signal is generated; If the real-time humidity of the grain depot does not belong to the management humidity range, when the real-time humidity of the grain depot is less than the left end point of the management humidity range, a low humidity signal is generated; when the real-time humidity of the grain depot is greater than the right end point of the management humidity range, a high humidity signal is generated; If the oxygen content per unit volume does not belong to the managed oxygen content interval, when the oxygen content per unit volume is less than the left end point of the managed oxygen content interval, a low oxygen signal is generated; when the oxygen content per unit volume is greater than the right end point of the managed oxygen content interval, a high oxygen signal is generated; If the unit volume carbon dioxide content does not belong to the managed carbon dioxide content interval, when the unit volume carbon dioxide content is less than the left end point of the managed carbon dioxide content interval, a low carbon dioxide signal is generated; when the unit volume carbon dioxide content is greater than the right end point of the managed carbon dioxide content interval, a high carbon dioxide signal is generated; In practice, a low temperature signal may be generated simultaneously with a low oxygen signal, and at the same time, a low temperature signal, a low humidity signal, and a high oxygen signal may also be generated simultaneously; The intelligent early warning module sends normal signals or various abnormal signals to the intelligent control module and the grain condition display module.

[0027] In this embodiment, the grain status 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: If a normal signal is received, the physical grain depot's corresponding twin model will be displayed in green; If a high temperature signal or a low temperature signal is received, the twin model of the physical grain depot corresponding to the grain depot will be displayed in yellow; If a high humidity signal or a low humidity signal is received, the physical grain depot's corresponding twin model will be displayed in blue; If a high oxygen signal or a low oxygen signal is received, the twin model of the physical grain depot corresponding to the grain depot will be displayed in purple; If a high carbon dioxide signal or a low carbon dioxide signal is received, the twin model of the physical grain depot corresponding to the grain depot will be displayed in red; Specifically, the intelligent control module is used to intelligently control the storage status in the physical grain warehouse, and the intelligent control process is as follows: If a high temperature signal is received, a cooling command is generated and sent to the control box, and the control box controls the axial flow fan to work according to the cooling command; If a low temperature signal is received, a temperature increase instruction is generated and sent to the control box, and the control box controls the heater to work according to the temperature increase instruction; If a high humidity signal is received, a dehumidification command is generated and sent to the control box, and the control box controls the dehumidifier to work according to the dehumidification command; If a low humidity signal is received, a humidification command is generated and sent to the control box, and the control box controls the humidifier to work according to the humidification command; If a high oxygen signal is received, a deoxygenation instruction is generated and sent to the control box, and the control box controls the deoxygenation device to work according to the deoxygenation instruction; If a low oxygen signal is received, an oxygen increase instruction is generated and sent to the control box, and the control box controls the aerator to work according to the oxygen increase instruction; If a high carbon dioxide signal is received, a decarbonization instruction is generated and sent to the control box, and the control box controls the carbon dioxide purifier to work according to the decarbonization instruction; If a low carbon dioxide signal is received, a carbon increase instruction is generated and sent to the control box, and the control box controls the carbon dioxide generator to work according to the carbon increase instruction.

[0028] Practical, such as Figure 2 The figure shows the workflow of the second twin grain depot (twin model of grain depot) controlling the physical grain depot. The network connection between the physical grain depot and the second twin grain depot (twin model of grain depot) is the connection between the human-computer interaction layer and the virtual action layer, and the connection between the virtual action layer and the physical equipment layer. The human-computer interaction layer includes the user interaction interface and the second twin grain depot, the virtual action layer includes the script parsing unit and the three-dimensional action unit, and the physical equipment layer includes the axial flow fan, dehumidifier and control box, etc. First, a control request is initiated in the user interaction interface according to the grain warehouse operation requirements, triggering the control script to transmit the target action data to the second twin grain warehouse. 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 warehouse through the communication protocol, and the equipment completes the corresponding ventilation and dehumidification switch 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 warehouse to synchronously complete the switch action. In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0029] Embodiment 2: Another concept based on the same invention, such as Figure 3 As shown, this embodiment proposes a grain warehouse intelligent method based on digital twins, and the method is as follows: Step S100, sampling grain stored in a physical grain warehouse to obtain multiple grain samples; Step S200, analyzing the grain samples in the physical grain warehouse 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 warehouse; Step S300, collecting real-time storage data of the physical grain depot, and combining the real-time storage data to perform intelligent early warning on the storage status of grain in the physical grain depot, and the early warning generates normal signals or various abnormal signals; Step S400, obtaining a twin model of a grain depot corresponding to the physical grain depot according to the 3D structure diagram; Step S500, displaying the corresponding twin model of the physical grain warehouse according to different signals, and intelligently controlling the storage status in the physical grain warehouse.

[0030] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The grain depot intelligent system based on digital twins includes a physical grain depot, a data acquisition module, a database and a digital twin platform, which is characterized by: The digital twin platform includes a model generation module, a sampling module, an intelligent analysis module, an intelligent early warning module, a grain status display module, and an intelligent control module; The database is used to store the 3D structure diagram of the physical grain depot, and the model generation module is used to generate a grain depot twin model corresponding to the physical grain depot, and send the grain depot twin model to the grain status display module; The sampling module is used to sample the grain stored in the physical grain warehouse to obtain multiple grain samples; the intelligent analysis module is used to analyze the grain samples in the physical grain warehouse 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 warehouse 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 for 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 status display module; The grain status display module is used to display the corresponding grain depot twin model in the physical grain depot based on different signals, and the intelligent control module is used to intelligently control the storage status of the physical grain depot.

2. The grain depot intelligent system based on digital twin according to claim 1 is characterized in that: The analysis process of the intelligent analysis module is as follows: Step P101, placing a grain sample in a simulated granary to obtain an initial insect content per unit volume of the corresponding grain sample; Step P102: if the initial insect content per unit volume is zero, the current real-time temperature value and real-time humidity value of the simulated granary are recorded as the suitable temperature node and the suitable humidity node respectively; If the initial insect content per unit volume is not zero, the real-time temperature and humidity of the simulated granary are reduced so that the real-time temperature and humidity of the simulated granary are maintained for a fixed period of time; Step P103, then obtaining the real-time insect content per unit volume of the corresponding grain sample in the simulated granary; If the real-time insect content per unit volume of the grain sample is equal to zero, the current real-time temperature value and real-time humidity value of the simulated granary are recorded as the suitable temperature node and the suitable humidity node respectively; If the real-time insect content per unit volume of the grain sample is not equal to zero, the real-time insect content per unit volume is compared with the initial insect content per unit volume. When the real-time insect content per unit volume is greater than or equal to the initial insect content per unit volume, no operation is performed. When the real-time insect content per unit volume is less than the initial insect content per unit volume, 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 value and real-time humidity value of the simulated granary, so that the real-time temperature value and real-time humidity value of the simulated granary are maintained for a fixed time, and repeat step P103 to obtain multiple groups of suitable temperature nodes and suitable humidity nodes; Step P105, if there are multiple groups of suitable temperature nodes and suitable humidity nodes, traverse and compare the multiple groups of suitable temperature nodes to obtain the minimum and maximum values ​​in the suitable temperature nodes, use the minimum value in the suitable temperature nodes as the left endpoint, use the maximum value in the suitable temperature nodes as the right endpoint, and construct the first suitable temperature interval of the grain sample based on the left endpoint and the right endpoint. Similarly, traverse and compare the multiple groups of suitable humidity nodes to obtain the minimum and maximum values ​​in the suitable humidity nodes, use the minimum value in the suitable humidity nodes as the left endpoint, use the maximum value in the suitable humidity nodes as the right endpoint, and construct the first suitable humidity interval 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, go to step P112.

3. The grain depot intelligent system based on digital twin according to claim 2 is characterized in that: The analysis process of the intelligent analysis module also includes: Step P106, placing another grain sample in a simulated granary to obtain an initial moldy amount per unit volume of the corresponding grain sample; Step P107, if the initial unit volume moldy amount is zero, then the current real-time temperature value and real-time humidity value of the simulated granary are recorded as the suitable temperature node and the suitable humidity node respectively; If the initial moldy amount per unit volume is not zero, the real-time temperature value and the real-time humidity value of the simulated granary are reduced so that the real-time temperature value and the real-time humidity value of the simulated granary are maintained for a fixed period of time; Step P108, then obtaining the real-time unit volume moldy amount of the corresponding grain sample in the simulated granary; If the real-time unit volume moldy amount of the grain sample is equal to zero, the current real-time temperature value and real-time humidity value of the simulated granary are recorded as the suitable temperature node and the suitable humidity node respectively; If the real-time moldy amount per unit volume of the grain sample is not equal to zero, the real-time moldy amount per unit volume is compared with the initial moldy amount per unit volume. When the real-time moldy amount per unit volume is greater than or equal to the initial moldy amount per unit volume, no operation is performed. When the real-time moldy amount per unit volume is less than the initial moldy amount per unit volume, the current real-time moldy amount per unit volume is replaced with the initial moldy amount per unit volume. Step P109, and so on, further reduce the real-time temperature value and real-time humidity value of the simulated granary, so that the real-time temperature value and real-time humidity value of the simulated granary are maintained for a fixed time, and repeat step P108 to obtain multiple groups of suitable temperature nodes and suitable humidity nodes; Step P110, if there are multiple groups of suitable temperature nodes and suitable humidity nodes, traverse and compare the multiple groups of suitable temperature nodes to obtain the minimum and maximum values ​​in the suitable temperature nodes, use the minimum value in the suitable temperature nodes as the left endpoint, use the maximum value in the suitable temperature nodes as the right endpoint, and construct the second suitable temperature interval of the grain sample based on the left endpoint and the right endpoint. Similarly, traverse and compare the multiple groups of suitable humidity nodes to obtain the minimum and maximum values ​​in the suitable humidity nodes, use the minimum value in the suitable humidity nodes as the left endpoint, use the maximum value in the suitable humidity nodes as the right endpoint, and construct the second suitable temperature interval and the second suitable humidity interval 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, go to step P112.

4. The grain depot intelligent system based on digital twin according to claim 3 is characterized in that: The analysis process of the intelligent analysis module also includes: Step P111, obtaining the first suitable temperature interval, the second suitable temperature interval, the first suitable humidity interval and the second suitable humidity interval, taking the intersection of the first suitable temperature interval and the second suitable temperature interval as the management temperature interval of the grain sample, and similarly, taking the intersection of the first suitable humidity interval and the second suitable humidity interval as the management humidity interval of the grain sample; Step P112, compare the sizes of the two groups of suitable temperature nodes, take the smaller value of the two groups of suitable temperature nodes as the left endpoint, take the larger value of the two groups 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 sizes of the two groups of suitable humidity nodes, take the smaller value of the two groups of suitable humidity nodes as the left endpoint, take the larger value of the two groups 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 grain depot intelligent system based on digital twin according to claim 4 is characterized in that: The analysis process of the intelligent analysis module also includes: Step P113, placing a grain sample in a simulated granary to obtain an initial insect content per unit volume of the corresponding grain sample; Step P114, if the initial insect content per unit volume is zero, the current real-time oxygen content and real-time carbon dioxide content of the simulated granary are recorded as the appropriate oxygen content node and the appropriate carbon dioxide content node respectively; If the initial insect content per unit volume is not zero, the real-time oxygen content in the simulated granary is reduced, and the real-time carbon dioxide content in the simulated granary is increased, so that the real-time oxygen content and the real-time carbon dioxide content in the simulated granary are maintained for a fixed period of time, and the next step is entered at the same time; Step P115, repeat steps P103-P112 to obtain the managed oxygen content range and the managed carbon dioxide content range of the grain sample.

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

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

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

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

10. The grain depot intelligent system based on digital twin according to claim 9 is characterized in that: The intelligent control process of the intelligent control module also includes: If a high oxygen signal is received, a deoxygenation instruction is generated and sent to the control box, and the control box controls the deoxygenation device to work according to the deoxygenation instruction; If a low oxygen signal is received, an oxygen increase instruction is generated and sent to the control box, and the control box controls the aerator to work according to the oxygen increase instruction; If a high carbon dioxide signal is received, a decarbonization instruction is generated and sent to the control box, and the control box controls the carbon dioxide purifier to work according to the decarbonization instruction; If a low carbon dioxide signal is received, a carbon increase instruction is generated and sent to the control box, and the control box controls the carbon dioxide generator to work according to the carbon increase instruction.

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