Grain supervision method, device and medium

Abstract

The application provides a grain supervision method and device and medium, and relates to the technical field of computers. The method comprises the following steps: identifying a vehicle entering a warehouse area, obtaining a grain rotation plan associated with the vehicle, generating a vehicle driving route requirement of the vehicle in the warehouse area, monitoring whether the vehicle drives in the warehouse area according to the vehicle driving route requirement, judging the consistency of the loading capacity of the vehicle, the change of the grain surface of the target warehouse before and after the vehicle loads and unloads the grain, and the grain rotation plan in response to the vehicle driving to a target warehouse according to the vehicle driving route requirement to load and unload the grain, starting a warehouse ventilation and air conditioning system according to internal condition monitoring data of the grain in the warehouse, formulating an in-warehouse operation plan according to the internal condition monitoring data, monitoring an operation personnel to perform an operation in the warehouse area according to the in-warehouse operation plan, and monitoring a warehouse grain surface change that is not allowed to be performed according to the internal condition monitoring data. The application connects multiple links of grain supervision in a grain depot, and realizes full-chain supervision of the grain in the grain depot.

Description

Technical Field

[0001] This application relates at least to the field of computer technology, and in particular to a method, apparatus and medium for grain monitoring. Background Technology

[0002] At present, grain purchase and sales have achieved preliminary informatization management. However, the supervision of purchase and sales involves various links such as grain depot entry and exit supervision, storage supervision, quality supervision, and transportation supervision. The data between these links are not connected and are independent of each system, forming data silos and making comprehensive analysis impossible.

[0003] Due to the existence of data silos, most of the current purchase and sales supervision relies on manual screening and review of information across the entire chain, which cannot be done in real time. It is usually judged after the fact, and it is impossible to issue early warnings before or during the process. Summary of the Invention

[0004] To address the aforementioned shortcomings, this application provides a grain supervision method, device, and medium to solve the following technical problem: how to connect multiple links in grain depot supervision and achieve full-chain supervision of grain in grain depots.

[0005] In a first aspect, this application provides a method for grain supervision, the method comprising:

[0006] Identify vehicles entering the storage area, obtain the grain rotation plan associated with the vehicles, generate the vehicle driving route requirements in the storage area, monitor whether the vehicles drive in the storage area according to the vehicle driving route requirements, and respond to the vehicles driving to the target warehouse to load and unload grain according to the vehicle driving route requirements, and determine the vehicle's grain loading capacity, the change in the grain surface of the target warehouse before and after the vehicle loads and unloads grain, and the consistency of the grain rotation plan.

[0007] Based on the internal monitoring data of the grain in the warehouse, the warehouse ventilation and air conditioning system is activated. Based on the internal monitoring data, an internal operation plan is formulated. The operation personnel are monitored to carry out the operation in accordance with the internal operation plan in the warehouse area. Any abnormal movement of the grain surface in the warehouse that is not allowed to be carried out according to the internal monitoring data is monitored.

[0008] Furthermore, the system identifies vehicles entering the storage area, obtains the grain rotation plan associated with each vehicle, generates route requirements for the vehicles within the storage area, and monitors whether the vehicles follow these route requirements within the storage area. Specifically, this includes:

[0009] In response to a vehicle's request to enter the storage area at the entrance / exit, the license plate recognition camera at the entrance / exit is activated to identify the license plate information, and the facial recognition camera at the registration point is activated to identify the vehicle owner information, and to determine whether the license plate information corresponds to a grain rotation plan;

[0010] If the license plate information corresponds to a grain rotation plan, obtain the weighing point and target warehouse corresponding to the grain rotation plan, and generate the vehicle driving route requirements for entering and leaving the warehouse area through the weighing point and target warehouse, including the optimal vehicle driving route, the first allowable range of vehicle stay time in the warehouse, and the second allowable range of vehicle stay time at key locations on the route.

[0011] The system sends the vehicle's driving route requirements to the vehicle, receives vehicle photos from lane cameras along the route in the storage area, and determines whether the vehicle is driving according to the driving route requirements. If the vehicle does not drive according to the optimal driving route, the actual time the vehicle spends in the storage exceeds the first allowable range, or the time the vehicle spends at key locations on the route exceeds the second allowable range, an alarm is issued.

[0012] Furthermore, in response to vehicles traveling to the target warehouse to load and unload grain according to the required route, the system determines the vehicle's grain loading capacity, the change in grain level in the target warehouse before and after loading and unloading, and the consistency with the grain rotation plan. Specifically, this includes:

[0013] In response to the requirement that the vehicle travels to the weighing station according to the driving route, the weighing station obtains the vehicle weighing information through the weighbridge, obtains the vehicle loading information through the detection camera, obtains the quality data of the grain on the vehicle through sampling and testing, and records the information of the weighing staff.

[0014] In response to the vehicle traveling to the target warehouse to load and unload grain according to the vehicle's driving route requirements, the intelligent PTZ camera in the target warehouse monitors the changes in the grain loading level and obtains grain quality analysis data, and estimates the changes in grain weight based on the changes in grain loading level.

[0015] The system compares the vehicle weighing information with the vehicle loading information, the grain weight information in the grain rotation plan, and the grain weight changes in the target warehouse. It also compares the on-board grain quality data with the quality requirements in the grain rotation plan and the grain quality analysis data in the target warehouse. If not, an alarm is issued.

[0016] Furthermore, in response to the vehicle's route requirement to reach the weighing station, the weighing station obtains the vehicle's weighing information via the weighbridge and acquires the vehicle's loading information via the detection camera, specifically including:

[0017] Before and after loading and unloading grain, the vehicle travels to the weighing station according to the vehicle's route requirements. The weighing station obtains the vehicle's tare weight and gross weight using a weighbridge. The net weight of the first load of grain is then obtained based on the vehicle's tare weight and gross weight.

[0018] The detection cameras located on the roof and rear of the vehicle at the weighing station identify the length, width, and height of the cargo compartment, as well as the type of grain inside, the grain level, and the grain quality grade. Based on the length, width, and height of the cargo compartment, the type of grain inside, and the grain level, the net weight of the second-loaded grain is estimated.

[0019] Furthermore, based on the internal monitoring data of the grain inside the warehouse, the warehouse ventilation and air conditioning systems are activated, and an operational plan for the warehouse is formulated based on the internal monitoring data, specifically including:

[0020] Temperature and humidity inside the warehouse and the grain are obtained by temperature and humidity sensors. The density of grain pests and mold is analyzed based on the video taken inside the warehouse. Combined with external temperature and humidity and seasonal data, the warehouse ventilation and air conditioning system is activated to ventilate and regulate the temperature of the warehouse.

[0021] Based on the temperature and humidity inside the warehouse, the temperature and humidity of the grain, and video footage taken inside the warehouse, if it is predicted that the grain inside the warehouse will be infested with pests or mold, an early warning of pests or mold will be issued and an in-warehouse operation plan will be formulated, which includes a warehouse inspection operation plan and a fumigation operation plan.

[0022] Furthermore, monitoring personnel within the warehouse area to perform tasks according to the warehouse operation plan includes:

[0023] The system activates all cameras within the warehouse area to identify the number of personnel conducting warehouse patrols and whether their patrol routes match the patrol plan. If not, an alarm is triggered.

[0024] The system activates cameras within the storage area to identify the number of personnel performing fumigation at designated fumigation sites, the distance between fumigation containers and chemical agents, and whether the actions taken to handle chemical residues after fumigation are consistent with the fumigation plan. If not, an alarm is issued.

[0025] Furthermore, monitoring of abnormal grain level changes in warehouses that are not permitted to be subject to inbound / outbound operations based on internal monitoring data specifically includes:

[0026] If the monitoring shows that the grain level in the warehouse is below the prescribed level for more than the allowed time, an early warning for exceeding the over-empty period will be issued.

[0027] If any unusual activity is detected in the grain storage without a rotation plan, an early warning will be issued for unauthorized use of reserve grain.

[0028] If abnormal movement of grain surface is detected in warehouse before the safe interval period has been completed after fumigation and a third-party quality inspection certificate is lacking, an early warning of illegal disposal of grain will be issued.

[0029] If the monitoring shows no change in the grain level in the target warehouse before and after vehicle loading and unloading, a false rotation warning will be issued;

[0030] If the monitoring shows that the vehicle's current inbound data corresponds to the vehicle's previous outbound data, a "circling grain" warning will be issued.

[0031] If the monitored vehicle's grain quality grade does not meet the grain quality standards, an early warning will be issued for grain exceeding the safety standards.

[0032] If the self-inspection quality data of grain in the warehouse is found to be inconsistent with the third-party quality inspection data of the grain, an abnormal quality inspection warning will be issued.

[0033] Secondly, this application provides a grain monitoring device, the device comprising:

[0034] The grain rotation monitoring module is used to identify vehicles entering the storage area, obtain the grain rotation plan associated with the vehicles, generate the vehicle driving route requirements in the storage area, monitor whether the vehicles drive in the storage area according to the vehicle driving route requirements, and respond to the vehicles driving to the target warehouse to load and unload grain according to the vehicle driving route requirements, and judge the grain loading capacity of the vehicles, the change of grain surface in the target warehouse before and after the vehicles load and unload grain, and the consistency of the grain rotation plan.

[0035] The warehouse internal situation monitoring module is used to activate the warehouse ventilation and air conditioning system based on the internal situation monitoring data of the grain in the warehouse, formulate warehouse operation plans based on the internal situation monitoring data, monitor the operation of the operation plan by the operators in the warehouse area, and monitor abnormal changes on the grain surface in the warehouse that are not allowed to be carried out according to the internal situation monitoring data.

[0036] Thirdly, this application provides a computer device including a processor and a memory, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the grain monitoring method as described above.

[0037] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the grain monitoring method described above.

[0038] This application provides a grain supervision method, device, and medium. By tracking the entire trajectory of vehicles entering the storage area, it strictly controls the vehicle's driving and grain loading and unloading operations within the storage area. Through multiple monitoring systems, it identifies the consistency of grain loading and unloading, achieving accurate control of grain rotation. At the same time, it monitors the real-time situation of grain within the storage area, ensuring that grain entering and leaving the storage area meets grain control requirements. This connects multiple links in grain storage supervision, achieving full-chain supervision of grain in the grain storage area. Attached Figure Description

[0039] Figure 1 This is a flowchart of a grain supervision method according to an embodiment of this application;

[0040] Figure 2 This is an architecture diagram of a grain monitoring system according to an embodiment of this application;

[0041] Figure 3 This is a schematic diagram of the structure of a grain monitoring device according to an embodiment of this application;

[0042] Figure 4 This is a schematic diagram of the structure of a computer device according to an embodiment of this application;

[0043] Figure 5 This is a schematic diagram of the structure of a computer-readable storage medium according to an embodiment of this application. Detailed Implementation

[0044] To enable those skilled in the art to better understand the technical solution of this application, the embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0045] It is understood that the specific embodiments and accompanying drawings described herein are merely for explaining this application and are not intended to limit this application.

[0046] It is understood that, without conflict, the various embodiments and features in the embodiments of this application can be combined with each other.

[0047] It is understood that, for ease of description, only the parts relevant to this application are shown in the accompanying drawings, while parts unrelated to this application are not shown in the drawings.

[0048] It is understood that each module or unit involved in the embodiments of this application may correspond to only one entity structure, or may be composed of multiple entity structures, or multiple modules or units may be integrated into one entity structure.

[0049] It is understood that, without conflict, the functions and steps indicated in the flowcharts and block diagrams of this application may occur in a different order than that indicated in the accompanying drawings.

[0050] It is understood that the flowcharts and block diagrams of this application illustrate the possible architecture, functions, and operations of systems, apparatuses, devices, and methods according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, unit, program segment, or code, containing executable instructions for implementing the specified function. Furthermore, each block or combination of blocks in the block diagrams and flowcharts may be implemented using a hardware-based device to implement the specified function, or using a combination of hardware and computer instructions.

[0051] It is understood that the modules and units involved in the embodiments of this application can be implemented by software or by hardware. For example, the modules and units can be located in the processor.

[0052] Example 1:

[0053] like Figure 1 As shown, this application provides a method for grain supervision, the method comprising:

[0054] S1. Identify vehicles entering the storage area, obtain the grain rotation plan associated with the vehicles, generate the vehicle driving route requirements in the storage area, monitor whether the vehicles drive in the storage area according to the vehicle driving route requirements, and respond to the vehicles driving to the target warehouse to load and unload grain according to the vehicle driving route requirements, determine the vehicle's grain loading capacity, the change in the grain surface of the target warehouse before and after the vehicle loads and unloads grain, and the consistency of the grain rotation plan.

[0055] S2. Activate the warehouse ventilation and air conditioning system based on the internal monitoring data of the grain in the warehouse, formulate an internal operation plan based on the internal monitoring data, monitor the operation personnel to carry out the operation in the warehouse area in accordance with the internal operation plan, and monitor any abnormal changes in the grain surface in the warehouse that are not allowed to be carried out according to the internal monitoring data.

[0056] In this embodiment, the provided method strictly controls the driving and grain loading and unloading operations of vehicles entering the storage area by tracking their entire trajectory. It also achieves accurate control of grain rotation by identifying the consistency of grain loading and unloading through multiple monitoring systems. At the same time, it monitors the real-time situation of grain in the storage area, ensuring that the grain entering and leaving the storage area meets the grain control requirements. This connects multiple links in the grain storage supervision and achieves full-chain supervision of grain in the grain storage area.

[0057] Specifically, this embodiment provides a method and information system for grain purchase and sale supervision and early warning. In order to solve the problem that grain depots currently rely on manual supervision, it proposes to design a grain purchase and sale supervision system through an intelligent analysis engine and a full-process AI analysis and supervision platform, which reduces the workload of manual judgment and improves the timeliness and accuracy of alarms.

[0058] like Figure 2 As shown, in order to solve the problem of data silos in the process of grain purchase and sale supervision, this embodiment provides a systematic design scheme for supervision in the field of grain purchase and sale.

[0059] (1) The overall architecture of this system includes a perception layer, an IoT control platform, a data middle platform, an intelligent engine, a full-process AI analysis and supervision platform, and an application layer.

[0060] (2) The perception layer connects to various hardware monitoring devices of the grain depot, such as smart cameras, various smart card readers, ID card verification devices, weighbridges, temperature and humidity sensors, integrated control cabinets, smart ventilation equipment, industrial air conditioners, etc.

[0061] (3) The IoT control platform is responsible for connecting to various hardware devices in the sensing layer. At the same time, it integrates device data and collects it at the edge gateway, and then transfers the data to the data platform. The IoT control platform supports commonly used communication technologies including RFID, Wi-Fi, Bluetooth, Zigbee, LoRa, NB-IoT, etc., and can parse various common protocols and support rapid access to various private protocols.

[0062] (4) In addition to supporting data from the IoT control platform, the data platform also supports connecting to databases of various independent systems, uploading various files, and manually inputting data. In the data platform, data is deduplicated, cleaned, and then archived.

[0063] (5) The intelligent engine is one of the key parts of this design, supporting single-condition rule processing, multi-condition rule processing, and rule configuration via JSON. Single-condition rule configuration targets the single control of a variable, such as setting upper and lower limits for grain temperature, and triggering an alarm immediately if the temperature exceeds these limits. Multi-condition rule configuration targets the control of multiple variables and conditions, supporting AND, OR, and NOT operations between conditions. For example, in case of an alarm for inconsistencies between the rotation plan and the inbound / outbound records, the planned rotation quantity, the actual inbound record quantity, and the outbound record quantity need to be compared pairwise. If there is a deviation between the three data points and the deviation exceeds the warning value, the system will immediately trigger an alarm. Complex rule configuration (via JSON) allows for complex control of multiple variables, including pre-defined rules for data statistics, summation, averaging, variance calculation, etc., and supports SQL retrieval configuration.

[0064] (6) The full-process AI analysis and supervision platform is the second key part of this design. It is mainly based on the full-process visual supervision of grain. Through video surveillance cameras set at key locations such as entrances and exits, registration points, weighing points, lanes, and warehouses, as well as various smart IoT devices in the warehouse, it performs intelligent analysis and processing of video and monitoring data at each key node, identifies problems in the purchase and sale supervision process and issues warnings. It mainly includes vehicle full-process trajectory management, grain quality management, and intelligent warehouse management.

[0065] (7) The application layer includes functions such as data display, early warning processing, early warning tracking, handling results, early warning escalation, and handling scoring. Early warnings are classified and graded, with different categories and levels of warnings corresponding to different personnel. Early warning escalation rules are also set; if the same warning is repeatedly triggered, it will be escalated according to the preset rules. Furthermore, after the early warning handling is completed, the timeliness and results of the handling are scored and ranked. Early warnings that are not handled in a timely manner are analyzed and improved to enhance work efficiency.

[0066] Data in this system flows upwards sequentially through the perception layer, IoT control platform, data middleware, intelligent rule engine, AI analysis platform, and application layer. The perception layer acquires data periodically based on device capabilities; this frequency can be configured on the IoT control platform. Generally, balancing system load and actual business rule requirements, data is acquired every 10–15 minutes by default, but this can be adjusted according to actual business needs and the warehouse environment.

[0067] As a data collection window, the data platform supports multiple data import methods, aggregating data from various independent information systems and resolving the data silo problem. Furthermore, it supports importing historical paper voucher data via scanning, ensuring data continuity within the system.

[0068] (1) Obtain data from various IoT devices from the IoT control platform. This type of data requires the addition of new hardware devices or the integration with existing hardware devices.

[0069] (2) For data in existing grain information systems, such as warehouse entry and exit systems and business ERP systems, it is supported to directly import the data through tools such as Sqoop.

[0070] (3) For paper voucher data, it is supported to upload it to the data platform by scanning, taking photos, etc., and then the data content is parsed by OCR. After cleaning and confirmation, the data is collected into a unified database.

[0071] In one embodiment, the process includes identifying vehicles entering the storage area, obtaining the grain rotation plan associated with the vehicles, generating vehicle route requirements within the storage area, and monitoring whether vehicles are traveling according to the required routes within the storage area. Specifically, this includes:

[0072] In response to a vehicle's request to enter the storage area at the entrance / exit, the license plate recognition camera at the entrance / exit is activated to identify the license plate information, and the facial recognition camera at the registration point is activated to identify the vehicle owner information, and to determine whether the license plate information corresponds to a grain rotation plan;

[0073] If the license plate information corresponds to a grain rotation plan, obtain the weighing point and target warehouse corresponding to the grain rotation plan, and generate the vehicle driving route requirements for entering and leaving the warehouse area through the weighing point and target warehouse, including the optimal vehicle driving route, the first allowable range of vehicle stay time in the warehouse, and the second allowable range of vehicle stay time at key locations on the route.

[0074] The system sends the vehicle's driving route requirements to the vehicle, receives vehicle photos from lane cameras along the route in the storage area, and determines whether the vehicle is driving according to the driving route requirements. If the vehicle does not drive according to the optimal driving route, the actual time the vehicle spends in the storage exceeds the first allowable range, or the time the vehicle spends at key locations on the route exceeds the second allowable range, an alarm is issued.

[0075] In one embodiment, in response to a vehicle traveling to a target warehouse to load and unload grain according to its designated route, the system determines the vehicle's grain loading capacity, the change in grain level at the target warehouse before and after loading and unloading, and the consistency of the grain rotation plan. Specifically, this includes:

[0076] In response to the requirement that the vehicle travels to the weighing station according to the driving route, the weighing station obtains the vehicle weighing information through the weighbridge, obtains the vehicle loading information through the detection camera, obtains the quality data of the grain on the vehicle through sampling and testing, and records the information of the weighing staff.

[0077] In response to the vehicle traveling to the target warehouse to load and unload grain according to the vehicle's driving route requirements, the intelligent PTZ camera in the target warehouse monitors the changes in the grain loading level and obtains grain quality analysis data, and estimates the changes in grain weight based on the changes in grain loading level.

[0078] The system compares the vehicle weighing information with the vehicle loading information, the grain weight information in the grain rotation plan, and the grain weight changes in the target warehouse. It also compares the on-board grain quality data with the quality requirements in the grain rotation plan and the grain quality analysis data in the target warehouse. If not, an alarm is issued.

[0079] In one embodiment, in response to the vehicle arriving at the weighing station according to the required driving route, the weighing station obtains the vehicle's weighing information via a weighbridge and acquires the vehicle's loading information via a detection camera, specifically including:

[0080] Before and after loading and unloading grain, the vehicle travels to the weighing station according to the vehicle's route requirements. The weighing station obtains the vehicle's tare weight and gross weight using a weighbridge. The net weight of the first load of grain is then obtained based on the vehicle's tare weight and gross weight.

[0081] The detection cameras located on the roof and rear of the vehicle at the weighing station identify the length, width, and height of the cargo compartment, as well as the type of grain inside, the grain level, and the grain quality grade. Based on the length, width, and height of the cargo compartment, the type of grain inside, and the grain level, the net weight of the second-loaded grain is estimated.

[0082] In this embodiment, the full-process AI analysis and supervision platform mainly uses smart cameras and smart IoT devices to conduct full-process intelligent management of the operation vehicle trajectory, grain quality, and warehousing behavior.

[0083] (1) Set up smart cameras in the following locations to achieve full coverage of monitoring of key nodes in the warehouse.

[0084] A) Install license plate recognition cameras at entrances and exits to identify license plate information.

[0085] B) Install facial recognition cameras at the registration point to identify vehicle owner information.

[0086] C) Install three intelligent monitoring cameras at the weighing point, located at the front, rear, and roof of the vehicle, respectively, to identify vehicle and grain information.

[0087] D) Install vehicle monitoring cameras in the lanes to ensure full coverage of the parking area, in order to locate the driving trajectories of vehicles.

[0088] E) Install one or more intelligent PTZ cameras in the warehouse to identify the grain storage line, grain outlet, and grain surface, ensuring full coverage of the grain outlet.

[0089] (2) This system monitors and locates vehicles in the warehouse area to form a vehicle trajectory map and monitors the behavior of vehicles in the warehouse area throughout the process.

[0090] A) The system plans the entry and exit routes for each warehouse, creating a fixed driving route map. Vehicles entering and exiting the warehouse must follow this fixed route map.

[0091] B) At the entrance and exit, the license plate number is automatically recognized by the license plate recognition camera. At the same time, the system queries the purchase and sale behavior of the vehicle, the reservation warehouse information, the vehicle model and other information to form a route map of the vehicle entering and leaving the warehouse.

[0092] C) Surveillance cameras along the driving route need to take pictures and identify all passing vehicles, and compare them with the vehicle's driving route to confirm whether the vehicle is driving on a permitted road. If a vehicle enters an unpermitted road, an alarm should be triggered.

[0093] D) The system analyzes the vehicle's driving trajectory within the storage area, determines the storage time and the dwell time at key locations, and triggers an alarm if the predetermined time is exceeded.

[0094] (3) The weight and quality of grain are evaluated through video AI algorithms to assist in the management of grain quality upon entering the warehouse.

[0095] A) During the weighing process, the height of the truck bed is identified by the rear camera, and the length, width, type of grain, and height of the grain surface from the top of the truck bed are identified by the roof camera.

[0096] B) Estimate the volume of grain based on the length, width, height of the wagon and the grain loading height; obtain the bulk density range of each grade of grain based on the type of grain.

[0097] C) Calculate the bulk density and grade of the grain by estimating the grain volume and the actual weight, and compare it with the bulk density and grade of the actual sample test. If the bulk density deviation exceeds 20%, the warehouse keeper should be given a warning and it should be recommended to resample and test.

[0098] (4) This system manages the entire grain storage process by using intelligent sphere monitoring and intelligent grain temperature detection equipment in the warehouse, combined with identification algorithms.

[0099] A) Grain Surface Anomaly Monitoring: Daily photos are taken inside the warehouse to identify any abnormalities in the grain loading line height, grain outlet, and grain surface. Simultaneously, data from the inbound / outbound system is used to determine whether any abnormalities in the grain outlet or grain surface are within the normal inbound / outbound operations. If an abnormality is detected, an alarm is triggered.

[0100] Understandably, the accuracy of grain weights obtained through weighing and video recognition differs. A wider error range can be set for grain weights obtained through video recognition, while grain weights obtained through weighing are considered consistent if they fall within the corresponding range. Similarly, the accuracy of changes in the weight of grain on trucks and in grain warehouses obtained through video recognition also differs; a similar method can be used to identify their consistency. The same applies to quality identification. While video recognition of grain quality data has lower accuracy, it can cover a wider range of identification areas, whereas sampling and testing offer the opposite. The two methods complement each other to provide more accurate and comprehensive grain quality identification data.

[0101] In one implementation, the warehouse ventilation and air conditioning systems are activated based on internal monitoring data of the grain inside the warehouse, and an operational plan for the warehouse is formulated based on the internal monitoring data, specifically including:

[0102] Temperature and humidity inside the warehouse and the grain are obtained by temperature and humidity sensors. The density of grain pests and mold is analyzed based on the video taken inside the warehouse. Combined with external temperature and humidity and seasonal data, the warehouse ventilation and air conditioning system is activated to ventilate and regulate the temperature of the warehouse.

[0103] Based on the temperature and humidity inside the warehouse, the temperature and humidity of the grain, and video footage taken inside the warehouse, if it is predicted that the grain inside the warehouse will be infested with pests or mold, an early warning of pests or mold will be issued and an in-warehouse operation plan will be formulated, which includes a warehouse inspection operation plan and a fumigation operation plan.

[0104] In one embodiment, monitoring personnel perform tasks within the warehouse area according to the warehouse operation plan, specifically including:

[0105] The system activates all cameras within the warehouse area to identify the number of personnel conducting warehouse patrols and whether their patrol routes match the patrol plan. If not, an alarm is triggered.

[0106] The system activates cameras within the storage area to identify the number of personnel performing fumigation at designated fumigation sites, the distance between fumigation containers and chemical agents, and whether the actions taken to handle chemical residues after fumigation are consistent with the fumigation plan. If not, an alarm is issued.

[0107] In this embodiment, the grain storage period is relatively long. Taking wheat as an example, the general storage period for wheat is 5 years, which is a long time span. Some data is limited by hardware limitations, and the storage time is short or it is still recorded offline, which also causes difficulties and pain points in supervision.

[0108] Therefore, this system also includes the following management for the warehouse:

[0109] B) Safe Operation Monitoring: This system integrates with the grain depot operation plan within the storage and management system to monitor and provide early warnings for operations within the warehouse. Upon detecting personnel entering the warehouse, the surveillance cameras immediately begin recording video and identify the operational procedures according to safe operating regulations.

[0110] If there are warehouse patrol operations on that day, the number of personnel, whether they are wearing safety helmets, whether they are following the prescribed patrol route, and whether there are any abnormal behaviors such as falling to the ground should be identified.

[0111] If fumigation is carried out on the same day, the number of operators, fumigation containers, distance of chemical placement, and actions taken to handle chemical residues after fumigation should be identified and analyzed.

[0112] C) Pest Monitoring: According to the pre-determined plan, 360-degree video surveillance is conducted daily within the warehouse. AI analysis is used to analyze the video footage, and an alarm is triggered if traces of corn weevils, grain borers, etc., are detected. Simultaneously, the density of pests is estimated through video AI recognition.

[0113] The system constructs a dataset related to grain temperature, warehouse humidity, external temperature and humidity, season, pest types, and crop types based on historical data. Based on statistical models and artificial intelligence analysis, it generates a pest occurrence density curve.

[0114] Based on the actual temperature and humidity inside the warehouse, grain temperature, external temperature and humidity, seasonal data, etc., and by comparing them with the pest density occurrence curve, combined with recent historical pest monitoring data, the stage of pest occurrence is assessed, and ventilation or early warning measures are taken according to the predetermined measures.

[0115] D) Grain temperature monitoring: The temperature and humidity inside the grain are monitored and managed using intelligent temperature and humidity measuring cables. If the temperature and humidity exceed a predetermined threshold, an alarm will be triggered.

[0116] Meanwhile, based on historical temperature and humidity data, grain pile volume, and grain mold data, a grain temperature change model and a grain mold model are formed using statistical models and artificial intelligence analysis.

[0117] Based on the current temperature and humidity of the grain, the volume of the grain pile, and combined with grain temperature change models and grain mold models, the possibility of grain temperature changes and mold growth can be determined, and early warnings can be issued.

[0118] In one implementation, monitoring abnormal grain level changes in warehouses that are not permitted to be subject to inbound / outbound operations based on insider monitoring data specifically includes:

[0119] If the monitoring shows that the grain level in the warehouse is below the prescribed level for more than the allowed time, an early warning for exceeding the over-empty period will be issued.

[0120] If any unusual activity is detected in the grain storage without a rotation plan, an early warning will be issued for unauthorized use of reserve grain.

[0121] If abnormal movement of grain surface is detected in warehouse before the safe interval period has been completed after fumigation and a third-party quality inspection certificate is lacking, an early warning of illegal disposal of grain will be issued.

[0122] If the monitoring shows no change in the grain level in the target warehouse before and after vehicle loading and unloading, a false rotation warning will be issued;

[0123] If the monitoring shows that the vehicle's current inbound data corresponds to the vehicle's previous outbound data, a "circling grain" warning will be issued.

[0124] If the monitored vehicle's grain quality grade does not meet the grain quality standards, an early warning will be issued for grain exceeding the safety standards.

[0125] If the self-inspection quality data of grain in the warehouse is found to be inconsistent with the third-party quality inspection data of the grain, an abnormal quality inspection warning will be issued.

[0126] In this embodiment, the intelligent engine supports the following rule categories by default, and the rule parameters can be modified. Additionally, it supports custom rules for specific business applications through database field mapping, JSON configuration, SQL statements, and other methods.

[0127] (1) Inbound and outbound operations: including vehicle behavior abnormality warning, vehicle dwell time warning, and grain triple consistency warning.

[0128] A) Vehicle Behavior Anomaly Warning: Search the historical entry and exit data for the license plate number of the vehicle currently entering the warehouse to see if it has entered and exited the same warehouse area or different warehouse areas multiple times within the same time period (e.g., within 24 hours); if so, obtain the corresponding vehicle's entry and exit weighing data and entry and exit quality inspection data.

[0129] If the last transaction was an outbound operation, is the deviation between the net weight of the last outbound transaction and the net weight of this inbound transaction within the warning range? Was the last transaction an outbound of old grain, and is this transaction an inbound of new grain? Are the quality inspection data of the last outbound transaction and the quality inspection data of this inbound transaction partially similar?

[0130] If the last transaction was an inbound operation, were the net weight of the previous inbound transaction and the net weight of the current inbound transaction within the warning range? Were the quality inspection data of the previous inbound transaction abnormal?

[0131] If all conditions are met, an early warning will be issued.

[0132] B) Market alert for vehicle dwell time: When a vehicle leaves the warehouse and the card is returned, the dwell time is calculated, and an alarm is triggered if the dwell time exceeds the preset value.

[0133] C) Three-fold consistency early warning for grain: Search for data showing consistent gross weight, tare weight, and net weight of a single vehicle. If there are cases of the same storage area, the same license plate number, and the same weighing staff, an early warning will be issued.

[0134] (2) Grain quality inspection business: quality inspection data early warning, loss and gain early warning, warehouse self-inspection and third-party quality inspection data comparison early warning.

[0135] A) Quality Inspection Data Early Warning: When inspecting incoming goods, compare the inspection value and the indicator value to see if there is a deviation. If the deviation exceeds the predetermined value, an alarm will be triggered to indicate that the quality is unqualified.

[0136] B) Loss and Gain Warning: Loss data is calculated according to the rules of the State Grain Administration. If the loss exceeds the local standard or there is an overconsumption, an early warning will be issued.

[0137] C) Warning based on comparison of in-warehouse self-inspection and third-party quality inspection data: If the difference between the in-warehouse self-inspection results and the third-party quality inspection results for the same batch of grain in the same storage location exceeds the predetermined value, a warning will be issued.

[0138] (3) Warehousing and storage business: Early warning of storage inspection frequency.

[0139] Storage Inspection Frequency Warning: In accordance with the daily inspection requirements of various regions, inspection frequencies are set for grain warehouse patrols, ventilation inspections, monthly inspections, and spring and autumn warehouse self-inspections. Statistics are compiled on a monthly and quarterly basis. If the current storage location is in a closed state and inspections are not carried out as required, an early warning will be issued.

[0140] (4) Purchase and sale business: unauthorized use of reserve grain beyond the designated period, false rotation, circular grain, and illegal disposal of grain with food safety indicators exceeding the standard.

[0141] A) Exceeding the approved idle period: Calculate the final deadline for rotation in accordance with the idle period requirements based on the rotation data for each batch; if the prescribed amount of grain is not rotated in before the deadline and no application for delayed rotation is submitted, an early warning will be issued.

[0142] B) Unauthorized use of reserve grain: If any outbound data or abnormal grain movement is found without approval of a rotation plan, an early warning will be issued for unauthorized use of reserve grain.

[0143] C) False Rotation: If there is a rotation plan and it is found that there are outbound records during the rotation cycle, but the grain surface has not been moved, a false rotation warning will be issued.

[0144] D) Circular Grain: If the same vehicle leaves one warehouse area and enters another warehouse area within a specified time, and the difference in net weight between the entry and exit is within the warning range, an early warning will be issued.

[0145] E) Improper handling of grain with excessive food safety indicators: If the quality inspection results of the grain leaving the warehouse show that the food safety indicators exceed the standards, and there is any outbound movement or abnormal movement of the grain at that location, an early warning will be issued.

[0146] This embodiment provides a complete system architecture, extracting and integrating data from each stage to avoid data gaps and data silos. It performs intelligent analysis and processing of vehicle trajectory management, grain quality management, and intelligent warehousing management, identifying problems in the purchase and sales supervision process and issuing early warnings. Through well-defined data analysis algorithms, it extracts and summarizes abnormal data from each stage, reducing the workload of manual analysis. Through process supervision and algorithm analysis, it forms a supervisory capability of pre-emptive prevention and in-process monitoring, minimizing the risk of problems and enabling rapid alarms and intelligent handling after problems occur, reducing losses.

[0147] Example 2:

[0148] like Figure 3 As shown, this application provides a grain monitoring device, the device comprising:

[0149] The grain rotation monitoring module 1 is used to identify vehicles entering the storage area, obtain the grain rotation plan associated with the vehicle, generate the vehicle driving route requirements in the storage area, monitor whether the vehicle drives in the storage area according to the vehicle driving route requirements, and, in response to the vehicle driving to the target warehouse to load and unload grain according to the vehicle driving route requirements, determine the vehicle's loaded grain capacity, the change in the grain surface of the target warehouse before and after the vehicle loads and unloads grain, and the consistency of the grain rotation plan.

[0150] The warehouse internal situation monitoring module 2 is used to activate the warehouse ventilation and air conditioning system based on the internal situation monitoring data of the grain in the warehouse, formulate the warehouse operation plan based on the internal situation monitoring data, monitor the operation of the operation personnel in the warehouse according to the warehouse operation plan, and monitor abnormal changes on the grain surface in the warehouse that are not allowed to be carried out according to the internal situation monitoring data.

[0151] In one embodiment, the grain rotation monitoring module 1 specifically includes:

[0152] The entrance and exit recognition unit is used to respond to a vehicle's request to enter the warehouse area when it arrives at the entrance and exit. It activates the license plate recognition camera set at the entrance and exit to recognize the license plate information, activates the facial recognition camera set at the registration point to recognize the vehicle owner information, and determines whether the license plate information corresponds to a grain rotation plan.

[0153] The route requirement generation unit is connected to the entrance and exit recognition unit. If the license plate information corresponds to a grain rotation plan, it is used to obtain the weighing point and target warehouse corresponding to the grain rotation plan, and generate the vehicle driving route requirements for entering and exiting the warehouse area through the weighing point and target warehouse. This includes the optimal vehicle driving route, the first allowable range of vehicle time in the warehouse, and the second allowable range of vehicle time spent at key locations on the route.

[0154] The monitoring unit along the route is connected to the route requirement generation unit. It sends the vehicle driving route requirements to the vehicle, receives vehicle photos sent by lane cameras set up along the route in the storage area, and determines whether the vehicle is driving according to the vehicle driving route requirements. If the vehicle does not drive according to the optimal vehicle driving route, the actual time the vehicle spends in the storage exceeds the first allowable range, or the time the vehicle stays at key locations on the route exceeds the second allowable range, an alarm prompt is issued.

[0155] In one embodiment, the grain rotation monitoring module 1 specifically includes:

[0156] The weighing unit is used to respond to the requirement that the vehicle travels to the weighing point according to the driving route. The weighing point obtains the vehicle weighing information through the weighbridge, obtains the vehicle loading information through the detection camera, obtains the quality data of the grain on the vehicle through sampling and testing, and records the information of the weighing staff.

[0157] The warehouse monitoring unit is used to respond to vehicles traveling to the target warehouse to load and unload grain according to the vehicle's driving route requirements. The intelligent PTZ camera in the target warehouse monitors the changes in the grain loading height and obtains grain quality analysis data. The grain weight change is estimated based on the changes in grain loading height.

[0158] The comparison and judgment unit is connected to the weighing unit and the warehouse monitoring unit. It is used to compare whether the vehicle weighing information is consistent with the vehicle loading information, the grain weight information in the grain rotation plan, and the grain weight change in the target warehouse. It also compares whether the on-board grain quality data is consistent with the quality requirements in the grain rotation plan and the grain quality analysis data in the target warehouse. If not, an alarm is issued.

[0159] In one embodiment, the weighing unit specifically includes components for:

[0160] Before and after loading and unloading grain, the vehicle travels to the weighing station according to the vehicle's route requirements. The weighing station obtains the vehicle's tare weight and gross weight using a weighbridge. The net weight of the first load of grain is then obtained based on the vehicle's tare weight and gross weight.

[0161] The detection cameras located on the roof and rear of the vehicle at the weighing station identify the length, width, and height of the cargo compartment, as well as the type of grain inside, the grain level, and the grain quality grade. Based on the length, width, and height of the cargo compartment, the type of grain inside, and the grain level, the net weight of the second-loaded grain is estimated.

[0162] In one embodiment, the reservoir area monitoring module 2 specifically includes:

[0163] The automatic control unit is used to obtain the temperature and humidity inside the warehouse and the temperature and humidity of the grain through temperature and humidity sensors. Based on the video taken inside the warehouse, it analyzes the density of grain pests and mold, and combined with external temperature and humidity and seasonal data, it starts the warehouse ventilation and air conditioning system to ventilate and regulate the temperature of the warehouse.

[0164] The manual control unit is used to respond to predictions of pests or mold in the grain in the warehouse based on the temperature and humidity inside the warehouse, the temperature and humidity of the grain, and video footage taken inside the warehouse. It issues pest or mold warnings and formulates a warehouse operation plan that includes a warehouse inspection plan and a fumigation plan.

[0165] In one embodiment, the reservoir area monitoring module 2 specifically includes:

[0166] The warehouse patrol monitoring unit is used to activate the cameras in the warehouse area to identify the number of personnel conducting warehouse patrols and whether the patrol routes are consistent with the warehouse patrol plan. If not, an alarm is issued.

[0167] The fumigation monitoring unit is used to activate cameras within the storage area to identify the number of fumigation workers at designated fumigation sites, the distance between fumigation containers and chemical agents, and whether the handling of chemical residues after fumigation is consistent with the fumigation plan. If not, an alarm is issued.

[0168] In one embodiment, the reservoir area monitoring module 2 includes an early warning unit, specifically used for:

[0169] If the monitoring shows that the grain level in the warehouse is below the prescribed level for more than the allowed time, an early warning for exceeding the over-empty period will be issued.

[0170] If any unusual activity is detected in the grain storage without a rotation plan, an early warning will be issued for unauthorized use of reserve grain.

[0171] If abnormal movement of grain surface is detected in warehouse before the safe interval period has been completed after fumigation and a third-party quality inspection certificate is lacking, an early warning of illegal disposal of grain will be issued.

[0172] If the monitoring shows no change in the grain level in the target warehouse before and after vehicle loading and unloading, a false rotation warning will be issued;

[0173] If the monitoring shows that the vehicle's current inbound data corresponds to the vehicle's previous outbound data, a "circling grain" warning will be issued.

[0174] If the monitored vehicle's grain quality grade does not meet the grain quality standards, an early warning will be issued for grain exceeding the safety standards.

[0175] If the self-inspection quality data of grain in the warehouse is found to be inconsistent with the third-party quality inspection data of the grain, an abnormal quality inspection warning will be issued.

[0176] Example 3:

[0177] like Figure 4 As shown, Embodiment 3 of this application provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the grain monitoring method as described in Embodiment 1. This computer device can be the grain monitoring device as described in Embodiment 2.

[0178] The memory is connected to the processor. The memory can be flash memory, read-only memory or other types of memory. The processor can be a central processing unit or a microcontroller.

[0179] Example 4:

[0180] like Figure 5 As shown, Embodiment 4 of this application provides a computer-readable storage medium storing a computer program. When the computer program is run by a processor, it implements the grain supervision method as described in Embodiment 1.

[0181] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program units, or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory or other memory technologies, CD-ROM (Compact Disc Read-Only Memory), DVD or other optical disc storage, cartridges, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer.

[0182] Embodiments 1-4 of this application provide a grain supervision method, device, and medium. By tracking the entire trajectory of vehicles entering the storage area, the method strictly controls the vehicle's driving and grain loading and unloading operations within the storage area. Through multiple monitoring methods, the method identifies the consistency of grain loading and unloading, thereby achieving accurate control of grain rotation. At the same time, it monitors the real-time situation of grain within the storage area, ensuring that the grain entering and leaving the storage area meets the grain control requirements. This method connects multiple links in grain storage supervision, achieving full-chain supervision of grain in the grain storage area.

[0183] It is understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of this application, and this application is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this application, and these modifications and improvements are also considered to be within the scope of protection of this application.

Claims

1. A method for grain supervision, characterized in that, The method includes: Identify vehicles entering the storage area, obtain the grain rotation plan associated with the vehicles, generate the vehicle driving route requirements in the storage area, monitor whether the vehicles drive in the storage area according to the vehicle driving route requirements, and respond to the vehicles driving to the target warehouse to load and unload grain according to the vehicle driving route requirements, and determine the vehicle's grain loading capacity, the change in the grain surface of the target warehouse before and after the vehicle loads and unloads grain, and the consistency of the grain rotation plan. Based on the internal monitoring data of the grain in the warehouse, the warehouse ventilation and air conditioning system is activated. Based on the internal monitoring data, an internal operation plan is formulated. The operation personnel are monitored to carry out the operation in accordance with the internal operation plan in the warehouse area. Any abnormal movement of the grain surface in the warehouse that is not allowed to be carried out according to the internal monitoring data is monitored.

2. The method according to claim 1, characterized in that, Identify vehicles entering the storage area, obtain the grain rotation plan associated with the vehicles, generate vehicle route requirements within the storage area, and monitor whether vehicles follow the required routes within the storage area. Specifically, this includes: In response to a vehicle's request to enter the storage area at the entrance / exit, the license plate recognition camera at the entrance / exit is activated to identify the license plate information, and the facial recognition camera at the registration point is activated to identify the vehicle owner information, and to determine whether the license plate information corresponds to a grain rotation plan; If the license plate information corresponds to a grain rotation plan, obtain the weighing point and target warehouse corresponding to the grain rotation plan, and generate the vehicle driving route requirements for entering and leaving the warehouse area through the weighing point and target warehouse, including the optimal vehicle driving route, the first allowable range of vehicle stay time in the warehouse, and the second allowable range of vehicle stay time at key locations on the route. The system sends the vehicle's driving route requirements to the vehicle, receives vehicle photos from lane cameras along the route in the storage area, and determines whether the vehicle is driving according to the driving route requirements. If the vehicle does not drive according to the optimal driving route, the actual time the vehicle spends in the storage exceeds the first allowable range, or the time the vehicle spends at key locations on the route exceeds the second allowable range, an alarm is issued.

3. The method according to claim 2, characterized in that, In response to vehicles traveling to the target warehouse to load and unload grain according to the required route, the system assesses the vehicle's grain loading capacity, the change in grain level in the target warehouse before and after loading and unloading, and the consistency with the grain rotation plan. Specifically, this includes: In response to the requirement that the vehicle travels to the weighing station according to the driving route, the weighing station obtains the vehicle weighing information through the weighbridge, obtains the vehicle loading information through the detection camera, obtains the quality data of the grain on the vehicle through sampling and testing, and records the information of the weighing staff. In response to the vehicle traveling to the target warehouse to load and unload grain according to the vehicle's driving route requirements, the intelligent PTZ camera in the target warehouse monitors the changes in the grain loading level and obtains grain quality analysis data, and estimates the changes in grain weight based on the changes in grain loading level. The system compares the vehicle weighing information with the vehicle loading information, the grain weight information in the grain rotation plan, and the grain weight changes in the target warehouse. It also compares the on-board grain quality data with the quality requirements in the grain rotation plan and the grain quality analysis data in the target warehouse. If not, an alarm is issued.

4. The method according to claim 3, characterized in that, Upon arrival at the weighing station according to the vehicle's designated route, the weighing station obtains the vehicle's weighing information via a weighbridge and its loading information via a detection camera, specifically including: Before and after loading and unloading grain, the vehicle travels to the weighing station according to the vehicle's route requirements. The weighing station obtains the vehicle's tare weight and gross weight using a weighbridge. The net weight of the first load of grain is then obtained based on the vehicle's tare weight and gross weight. The detection cameras located on the roof and rear of the vehicle at the weighing station identify the length, width, and height of the cargo compartment, as well as the type of grain inside, the grain level, and the grain quality grade. Based on the length, width, and height of the cargo compartment, the type of grain inside, and the grain level, the net weight of the second-loaded grain is estimated.

5. The method according to any one of claims 1-4, characterized in that, Based on the internal monitoring data of the grain inside the warehouse, activate the warehouse ventilation and air conditioning system, and formulate an internal operation plan based on the internal monitoring data, specifically including: Temperature and humidity inside the warehouse and the grain are obtained by temperature and humidity sensors. The density of grain pests and mold is analyzed based on the video taken inside the warehouse. Combined with external temperature and humidity and seasonal data, the warehouse ventilation and air conditioning system is activated to ventilate and regulate the temperature of the warehouse. Based on the temperature and humidity inside the warehouse, the temperature and humidity of the grain, and video footage taken inside the warehouse, if it is predicted that the grain inside the warehouse will be infested with pests or mold, an early warning of pests or mold will be issued and an in-warehouse operation plan will be formulated, which includes a warehouse inspection operation plan and a fumigation operation plan.

6. The method according to claim 5, characterized in that, Monitoring personnel are monitoring the work performed within the warehouse area according to the warehouse work plan, specifically including: The system activates all cameras within the warehouse area to identify the number of personnel conducting warehouse patrols and whether their patrol routes match the patrol plan. If not, an alarm is triggered. The system activates cameras within the storage area to identify the number of personnel performing fumigation at designated fumigation sites, the distance between fumigation containers and chemical agents, and whether the actions taken to handle chemical residues after fumigation are consistent with the fumigation plan. If not, an alarm is issued.

7. The method according to claim 6, characterized in that, Monitoring, based on insider data, indicates abnormal changes in grain levels in warehouses that would preclude inbound / outbound operations. Specifically, these include: If the monitoring shows that the grain level in the warehouse is below the prescribed level for more than the allowed time, an early warning for exceeding the over-empty period will be issued. If any unusual activity is detected in the grain storage without a rotation plan, an early warning will be issued for unauthorized use of reserve grain. If abnormal movement of grain surface is detected in warehouse before the safe interval period has been completed after fumigation and a third-party quality inspection certificate is lacking, an early warning of illegal disposal of grain will be issued. If the monitoring shows no change in the grain level in the target warehouse before and after vehicle loading and unloading, a false rotation warning will be issued; If the monitoring shows that the vehicle's current inbound data corresponds to the vehicle's previous outbound data, a "circling grain" warning will be issued. If the monitored vehicle's grain quality grade does not meet the grain quality standards, an early warning will be issued for grain exceeding the safety standards. If the self-inspection quality data of grain in the warehouse is found to be inconsistent with the third-party quality inspection data of the grain, an abnormal quality inspection warning will be issued.

8. A grain monitoring device, characterized in that, The device includes: The grain rotation monitoring module is used to identify vehicles entering the storage area, obtain the grain rotation plan associated with the vehicles, generate the vehicle driving route requirements in the storage area, monitor whether the vehicles drive in the storage area according to the vehicle driving route requirements, and respond to the vehicles driving to the target warehouse to load and unload grain according to the vehicle driving route requirements, and judge the grain loading capacity of the vehicles, the change of grain surface in the target warehouse before and after the vehicles load and unload grain, and the consistency of the grain rotation plan. The warehouse internal situation monitoring module is used to activate the warehouse ventilation and air conditioning system based on the internal situation monitoring data of the grain in the warehouse, formulate warehouse operation plans based on the internal situation monitoring data, monitor the operation of the operation plan by the operators in the warehouse area, and monitor abnormal changes on the grain surface in the warehouse that are not allowed to be carried out according to the internal situation monitoring data.

9. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing a computer program, and when the processor runs the computer program stored in the memory, the processor performs the grain monitoring method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the grain monitoring method as described in any one of claims 1-7.

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