A grain and oil supply chain intelligent management method and system

Abstract

The present application relates to the technical field of intelligent management of supply chain, and particularly discloses a kind of grain and oil supply chain intelligent management method and system, the method comprises: the physical weighing and risk initialization of grain and oil raw materials to factory are carried out, warehouse admission determination and shunting treatment are carried out, inventory weighing and risk continuous timing during warehousing are carried out, comprehensive inspection and processing shunting decision of grain and oil raw materials are carried out before delivery, the loading and shipping of grain and oil raw materials are carried out and transportation is started, in-transit estimated time calculation is carried out, and arrival check is carried out, the estimated time of arrival, arrival quality and transportation loss amount are determined, the problems of grain silo existing discharge arching, oil tank existing tank wall oil hanging and bottom deposition, actual physical adhesion and accumulation form, causing virtual volume trap are solved.The lag window problem exists in the aflatoxin production process, and the existing grain condition system only looks at the temperature and humidity threshold value, without the foresight model of the aflatoxin risk window.

Description

Technical Field

[0001] This invention relates to the field of intelligent supply chain management technology, specifically to an intelligent management method and system for the grain and oil supply chain. Background Technology

[0002] Currently, escalating regional trade frictions, intensified global climate change, and international geopolitical competition are intertwined and overlapping, profoundly impacting the stability of the global food industry and supply chains. Accelerating the construction of a comprehensive, intelligent, and precise food security risk identification, monitoring, and early warning system is not only an urgent need to address technological shortcomings and enhance food risk prevention and control capabilities, but also an essential aspect of developing new agricultural productivity and advancing the modernization of national security systems and capabilities.

[0003] Currently, research on intelligent management of the grain and oil supply chain still has some shortcomings. Specifically, grain silos experience arching during discharge, and oil storage tanks suffer from oil residue buildup on the tank walls and sediment at the bottom, creating virtual volume traps due to the actual physical adhesion and accumulation patterns. Furthermore, the aflatoxin production process has a lag window problem; existing grain condition systems only consider temperature and humidity thresholds and lack forward-looking models for the aflatoxin production risk window. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent management method and system for the grain and oil supply chain, which can effectively solve the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of the present invention provides an intelligent management method for the grain and oil supply chain, comprising the following steps:

[0006] Physical weighing and risk initialization are carried out on the grain and oil raw materials arriving at the factory, and warehousing permit determination and diversion processing are performed to determine the warehousing information of the grain and oil raw materials arriving at the factory.

[0007] Based on the information on grain and oil raw materials entering the warehouse, inventory weighing and continuous risk timing are performed during the storage period, and the information on grain and oil raw materials during the storage period is output.

[0008] By combining the information on grain and oil raw materials entering the factory and the information on grain and oil raw materials during storage, a comprehensive inspection and processing diversion decision is made on the grain and oil raw materials before they leave the warehouse, and the characteristic data of grain and oil raw materials leaving the warehouse are obtained.

[0009] Based on the outbound characteristic data of grain and oil raw materials, the loading and measurement of grain and oil raw materials are carried out and transportation is initiated, and the transportation information of grain and oil raw materials is output.

[0010] Based on the transportation information of grain and oil raw materials, the estimated arrival time in transit is calculated, and the arrival verification is carried out to determine the estimated arrival time, the quality of the goods, and the amount of transportation loss.

[0011] A second aspect of the present invention provides an intelligent management system for the grain and oil supply chain, comprising:

[0012] The raw material warehousing processing module is used to physically weigh and initialize the risk of grain and oil raw materials arriving at the factory, determine the warehousing permit and divert the raw materials, and determine the warehousing information of the grain and oil raw materials arriving at the factory.

[0013] The raw material storage analysis module is used to weigh the inventory and continuously time the risk during the storage period based on the information of grain and oil raw materials entering the factory, and output the information of grain and oil raw materials during the storage period.

[0014] The raw material outbound inspection module is used to combine the information on grain and oil raw materials entering the factory and the information on grain and oil raw materials during storage to perform comprehensive inspection and processing diversion decisions on grain and oil raw materials before they leave the warehouse, and obtain characteristic data of grain and oil raw materials leaving the warehouse.

[0015] The raw material transportation analysis module is used to measure the loading and transportation of grain and oil raw materials based on the outbound characteristic data of grain and oil raw materials, and output the transportation information of grain and oil raw materials.

[0016] The raw material transportation verification module is used to calculate the estimated arrival time in transit based on the transportation information of grain and oil raw materials, and to verify the arrival time, quality of goods, and amount of transportation loss.

[0017] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0018] (1) This invention provides a smart management method and system for the grain and oil supply chain. By conducting pre-inspection of physical weighing and risk initialization, quality risks are intercepted before the warehousing stage. By weighing inventory and continuously timing risks during storage, dual control over the true quality of inventory and the trend of quality evolution is achieved, thus solving the problems of inflated inventory and unpredictable quality changes in traditional management.

[0019] (2) This invention completes the final quality confirmation and problem diversion before the goods leave the warehouse through comprehensive inspection and processing diversion decision-making before the goods leave the warehouse, avoiding the flow of unqualified products into the downstream. By solidifying the information of loading measurement and transportation start-up, the transportation link has a complete data starting point. By calculating the expected arrival time in transit and verifying the arrival, the transportation process is transformed from uncontrollable to predictable and traceable. Attached Figure Description

[0020] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the method steps of the present invention.

[0022] Figure 2 This is a schematic diagram of the system module connections of the present invention.

[0023] Figure 3 This is a flowchart illustrating the multi-dimensional hierarchical determination of quality status and the output of outbound permission signals in accordance with the present invention. Detailed Implementation

[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0025] Reference Figure 1 As shown, the first aspect of the present invention provides an intelligent management method for the grain and oil supply chain, including: physically weighing and risk initializing the grain and oil raw materials arriving at the factory, determining the warehousing permit and diverting the materials, and determining the warehousing information of the grain and oil raw materials arriving at the factory.

[0026] The specific process for physically weighing and risk initialization of grain and oil raw materials arriving at the factory is as follows:

[0027] To obtain the total mass of raw materials entering the warehouse, after the transport vehicle enters the weighbridge, the weighbridge sensor outputs the gross weight value. After the vehicle unloads, it is weighed again under no-load conditions, and the weighbridge sensor outputs the tare weight value. The total mass of raw materials entering the warehouse is equal to the gross weight value minus the tare weight value.

[0028] Obtain the water activity and temperature values ​​at the time of raw material unloading. Use a water activity meter to obtain the water activity value (dimensionless) and a platinum resistance temperature sensor to obtain the temperature value (in degrees Celsius).

[0029] One pulse signal is generated every second. Each pulse rises at the moment the voltage in the square wave signal generated by the system hardware clock crystal oscillator changes from low to high. The real-time moisture activity and temperature values ​​of the raw material are acquired. It is determined whether the moisture activity value of the raw material is greater than the critical value for aflatoxin production and whether the temperature value is greater than the lower limit of aflatoxin production temperature. If both conditions are met, the initial risk accumulation seconds are incremented by one. If either condition is not met, the initial risk accumulation seconds remain unchanged. Sampling and measurement are performed immediately after unloading. The timer starts when the moisture activity and temperature values ​​are acquired, with an initial value of zero, and stops when the warehousing permission determination and diversion processing are completed. The initial risk accumulation seconds are output in seconds.

[0030] The critical water activity value for aflatoxin production by *Aspergillus flavus* is the minimum water activity required for *Aspergillus flavus* to grow and produce aflatoxin on a grain or oil substrate. When the water activity of the material is below this critical value, *Aspergillus flavus* cannot grow and therefore will not produce aflatoxin. Pitt et al. (1995) published a research paper entitled "Water Relations of *Aspergillus flavus* and Closely Related Species" in the *Journal of Food Protection*, Volume 58, Issue 1. This study determined the minimum water activity for germination and growth of *Aspergillus flavus* at three temperatures: 25°C, 30°C, and 37°C, using a glucose-fructose mixture to control the water activity of the culture medium (range 0.996 to 0.75). The results showed that the minimum growth water activity at 25°C was 0.82. Because the environmental requirements for toxin production metabolism are more stringent than those for mycelial growth, subsequent studies have confirmed that the actual toxin production threshold is usually around 0.85. However, in risk assessment and control engineering, 0.82 is often used as a conservative physical threshold.

[0031] The lower limit of aflatoxin production in Aspergillus flavus is the minimum ambient temperature required for Aspergillus flavus to produce aflatoxin. Below this temperature, even if the water activity meets the requirements, Aspergillus flavus cannot produce toxin. Schindler et al. (1967) published a research paper titled "Aflatoxin Production by Aspergillus flavusas Related to Various Temperatures" in *Applied Microbiology*, Volume 15, Issue 5. In this study, two toxin-producing Aspergillus strains were cultured on malt extract medium at 10 temperature points (2, 7, 13, 18, 24, 29, 35, 41, 46, and 52 degrees Celsius) for 5 days, and the aflatoxin production was measured. The results showed that no aflatoxin was detected after 5 days of culture at temperatures below 18 degrees Celsius; at 13 degrees Celsius, toxins were detected after extended culture for 3 weeks. This study covered a temperature gradient from 2 degrees Celsius to 52 degrees Celsius, establishing the low-temperature limit for aflatoxin production in Aspergillus flavus.

[0032] The specific process for determining the entry permit and sorting of raw grains and oils into the factory warehouse is as follows:

[0033] Retrieve the raw material name field and water activity value.

[0034] If the raw material belongs to the category of grains or oilseeds and its water activity value is greater than the water activity limit of refined oils, the raw material type is marked as applicable to the risk timer. When the water activity of grains and oilseeds is higher than the water activity limit of refined oils, molds have the moisture conditions for growth and reproduction, and their risk of toxicity needs to be monitored through the risk timer.

[0035] If the raw material belongs to the refined oil category and its water activity value is less than or equal to the water activity limit of refined oil, the raw material type is marked as not applicable to the risk timer. When the water activity of refined oil is less than or equal to the water activity limit of refined oil, mold cannot grow and reproduce inside it, and it does not need to be monitored by the risk timer.

[0036] If the raw material belongs to the grain or oilseed category and its water activity value is less than or equal to the water activity limit of refined oil, the raw material type is marked as an applicable risk timer. Although the current water activity is low, the porous structure of grains and oilseeds may cause the water activity of local areas to rise above the water activity limit of refined oil due to localized moisture absorption (such as condensation on the storage wall or moisture migration caused by temperature difference) during storage. After marking it as an applicable risk timer, continuous monitoring during storage can capture any changes in hazardous conditions in any local area.

[0037] If the raw material belongs to the category of refined oils and its water activity value is greater than the water activity limit of refined oils, the raw material type is marked as applicable to the risk timer, and an abnormal oil moisture warning is generated at the same time. Theoretically, the water activity of refined oils should be less than or equal to the water activity limit of refined oils. If the measured value is greater than the water activity limit of refined oils, it indicates that there is an abnormality in the refining and dehydration process of this batch of oils or that the oils have become damp during transportation. Oils in this state have the moisture conditions for mold growth and need to be included in the monitoring scope of the risk timer. At the same time, an additional inspection of the current raw material is carried out through a warning notification.

[0038] For raw materials that are not subject to risk timers, output directly and mark the warehousing permission signal logic as true.

[0039] Execution risk assessment logic:

[0040] For raw materials for which a risk timer is applicable, the communication interface reads the historical cumulative risk seconds from the upstream supply chain of the corresponding raw material, calculates the total cumulative risk seconds, which is equal to the initial cumulative risk seconds plus the historical cumulative risk seconds, and compares the total cumulative risk seconds with the lower limit of the physical lag time for toxicity production.

[0041] If the total accumulated risk seconds are less than the lower limit of the physical lag time for toxic production, the corresponding raw material warehousing permission signal logic is output as true, and the tightened judgment flag is set to no.

[0042] If the total accumulated risk seconds are greater than or equal to the lower limit of the physical lag time for toxicity production, calculate the risk over-limit multiple. The risk over-limit multiple is equal to the integer part of the quotient obtained by dividing the total accumulated risk seconds by the lower limit of the physical lag time for toxicity production. If the risk over-limit multiple is equal to 1, output the warehousing permission signal logic as false and generate a first-level forced physical processing instruction (the content includes the processing method as forced ventilation cooling or conditioning and dehumidification, and the processing duration as the standard processing time). If the risk over-limit multiple is greater than 1, output the warehousing permission signal logic as false and generate a quality scrap instruction.

[0043] After the forced physical processing instruction is executed, the water activity and temperature values ​​of the corresponding raw materials after processing are updated, the total cumulative risk seconds are calculated, and the risk judgment logic is executed again.

[0044] If the result is that the total risk accumulation in seconds is less than the lower limit of the physical lag time for toxic production, the tightened judgment flag is set to yes, the logic of the warehousing permission signal is set to true, and the corresponding raw material outbound judgment benchmark time is forcibly set to the tightened judgment benchmark time, which is half of the lower limit of the physical lag time for toxic production.

[0045] If the result is that the total risk accumulation in seconds is greater than or equal to the lower limit of the physical lag time for toxic production, the risk over-limit multiple is recalculated. The risk over-limit multiple is equal to the integer part of the quotient obtained by dividing the updated total risk accumulation in seconds by the lower limit of the physical lag time for toxic production.

[0046] If the risk exceedance factor is 1, retrieve the number of times the processing has been performed. If the number of processing times is less than 2, output the warehousing permission signal logic as false and generate a first-level forced physical processing instruction (the content includes the processing method as forced ventilation and cooling or conditioning and dehumidification, and the processing duration as the standard processing time). If the number of processing times has reached 2, determine that the current raw material has irreversibly entered the toxic state, output the warehousing permission signal logic as false, and generate a quality scrap instruction. The maximum number of processing times is set to 2 to avoid falling into a logic loop.

[0047] If the risk exceeds the limit by a factor of 2, the logic for issuing an inbound permit signal will be false, and a quality scrap instruction will be generated.

[0048] Output the grain and oil raw material warehousing information to the factory, including warehousing permission signal (logical true or logical false), tightened judgment flag (yes or no), raw material type flag (applicable risk timer or not applicable risk timer), total raw material warehousing mass, initial risk accumulation seconds, moisture activity value and temperature value, and record it to the grain and oil supply chain intelligent management log.

[0049] The water activity limit of refined oils is the upper limit of the water activity obtained after refining processes such as degumming, deacidification, decolorization, and deodorization. Below this water activity, microorganisms (including molds) cannot grow and reproduce in the oil.

[0050] The lower limit of the physical lag time for aflatoxin production is the shortest physical time required for Aspergillus flavus to go from being in a suitable environment for aflatoxin production to the first detection of aflatoxin. Before this time, even if the environmental conditions are perfectly suitable, the toxin has not yet been synthesized. Aspergillus flavus strains were inoculated on a grain / oil substrate and cultured under different temperature and humidity combinations. Samples were taken every few hours from the inoculation time to detect the aflatoxin content. The lag time under that condition was obtained by subtracting the inoculation time from the time of first toxin detection. The experiment was repeated under different temperature and humidity combinations, and the shortest lag time measured under all experimental conditions was taken as the lower limit of the physical lag time for aflatoxin production.

[0051] The standard processing time is the standard duration for forced ventilation cooling or conditioning and dehumidification treatment of non-conforming batches, derived from the standard work shift length of continuous industrial production operations. A standard work shift is 8 hours, and three shifts constitute a complete workday. This is a common time convention in enterprise production management.

[0052] In one specific embodiment, the total mass of raw materials entering the warehouse is obtained by weighing on a weighbridge. Moisture activity and temperature values ​​are obtained using a moisture activity meter and a platinum resistance temperature sensor. Based on this, an initial risk accumulation time is constructed, quantifying the physical time the material spends in a suitable toxic environment from unloading to completion of inspection. This allows subsequent processes to understand the risk level of the batch of raw materials before warehousing. A risk timer is used to determine whether it is applicable or not, based on a joint judgment of material category and moisture activity value, avoiding ineffective monitoring of low-risk materials such as refined oils. For raw materials for which a risk timer is applicable, the initial risk accumulation time is added to the historical risk accumulation time from upstream sources and compared with the lower limit of the physical lag time for toxic production. Graded processing is performed based on the risk exceeding the limit multiple. A cyclical exit mechanism is formed through processing limit and stricter judgment markers to ensure that materials exceeding the reversible range are promptly scrapped, and materials that pass processing are subject to stricter judgment criteria upon subsequent warehousing. The above process ensures that the decision to grant warehousing permits is based entirely on objective physical measurements and rigid logical comparisons, without relying on the subjective experience and judgment of operators. At the same time, it enables cross-stage traceability of quality risks through the chain transmission of historical risk seconds.

[0053] Based on the information on grain and oil raw materials entering the warehouse, inventory weighing and continuous risk timing are performed during the storage period, and the information on grain and oil raw materials during the storage period is output.

[0054] To obtain the true remaining mass of the raw materials, the total mass value of the tank is read from the pressure weighing sensor, and the cumulative outflow mass value is read from the Coriolis mass flow meter. The true remaining mass of the raw materials is equal to the total mass of the tank minus the cumulative outflow mass.

[0055] The difference between the actual remaining mass of raw materials in the previous second and the actual remaining mass of raw materials in the current second is recorded as the actual remaining mass difference. The cumulative outflow mass increment between the previous second and the current second is recorded as the outflow mass increment difference.

[0056] If the difference between the actual remaining mass difference and the outflow mass increment difference exceeds the mass conservation deviation tolerance value, a sensor consistency alarm is generated, and the inventory data is marked as pending calibration.

[0057] Every second, the water activity and temperature values ​​of all measuring points are read. If the water activity value of any measuring point is greater than the critical value of water activity for Aspergillus flavus toxin production and the temperature value is greater than the lower limit of temperature for Aspergillus flavus toxin production, the current second is determined to be a microenvironment with a risk of toxin production. The corresponding measuring point is recorded as a risk measuring point, and the risk area coverage rate is calculated. The risk area coverage rate is equal to the number of risk measuring points divided by the total number of measuring points.

[0058] The water activity sensor has a response time constant, which is the nominal value stated in the sensor's manufacturer's manual. Since this response time constant is much greater than one second, the value read by the system per second is the instantaneous value of the sensor's output signal within that second, reflecting the average environmental state within the sensor's response time constant, rather than the instantaneous changes within that second.

[0059] Obtain the current average water activity value and current average temperature value of all measuring points; determine the cumulative number of seconds of storage risk, the remaining safe time, and the graded early warning signals.

[0060] Perform conditional cross-checks every second:

[0061] Condition a: The discharge valve control signal is in the open command state (the valve is in the open state); Condition b: The total mass of the silo decreases by zero within the continuous blockage judgment time window (the total mass of the silo does not decrease); Condition c: The motor current value is greater than the motor stall current multiple multiplied by the rated current (the motor current abnormally rises to the stall current level).

[0062] If condition a is true, condition b is true, and condition c is true, it indicates that the discharge port is blocked by raw material. The motor drives the load to rotate, but the discharge port cannot discharge the raw material, resulting in motor overload. The output blockage status variable logic is true, and the blockage type label is marked as mechanical blockage.

[0063] If condition a is true, condition b is true, and condition c is false, it indicates that there is actually no raw material to be discharged from the tank, but the weighing sensor still shows that there is remaining mass. This is a typical virtual volume trap (arching or wall adhesion causes the raw material to be suspended, and the weighing sensor measures the mass of the suspended raw material, but there is no raw material flowing out of the discharge port). The output blockage status variable logic is true, and the blockage type label is marked as empty tank misjudgment.

[0064] If condition a is true, condition b is false, and condition c is true, it indicates that there is intermittent overload during the discharge process (such as the screw conveyor or pump being overloaded for a short time due to large foreign objects mixed in the raw material), but the raw material is still flowing out, the logic of the output blockage status variable is false, and a motor overload warning signal is generated.

[0065] If condition a is true, condition b is false, and condition c is false, it indicates that the outbound operation is proceeding normally without any abnormalities. The logic of the output blockage status variable is false, and the outbound process continues normally.

[0066] If condition a is false, condition b is true, and condition c is true, it indicates that although the valve is closed, the motor is still running (or there is a fault in the motor control circuit, causing the motor to continue to rotate after the valve is closed). The motor is in a stalled state, and the output blockage status variable logic is false (because there is no blockage in the discharge valve state), generating an abnormal alarm for the control circuit.

[0067] If condition a is false, condition b is true, and condition c is false, it indicates that the outbound operation has stopped, the equipment is in a shutdown or standby state, the output blockage status variable logic is false, and there is no operation.

[0068] If condition a is false, condition b is false, and condition c is true, there is a physical contradiction: raw materials should not flow out when the valve is closed, and the motor should not run when the valve is closed. This indicates that the valve is not actually completely closed (valve malfunction and leakage) or there is another discharge channel (pipeline damage). At the same time, the motor control is abnormal, the output blockage status variable logic is false, generating a discharge leakage alarm and a control loop abnormality alarm. If the duration after the valve is closed is less than the residual evacuation time (this time is equal to the pipeline volume divided by the rated flow rate), it is considered normal residual evacuation, and no leakage alarm is generated. If the tank mass continues to decrease after the residual evacuation time has passed, a discharge leakage alarm is generated.

[0069] If condition a is false, condition b is false, and condition c is false, there is a physical contradiction: the raw material should not flow out when the valve is closed, indicating that the valve is not actually completely closed (valve malfunction and leakage) or there is another discharge channel (pipeline damage). There is no motor overload signal, indicating that the raw material flows out without power (such as gravity flow or leakage after pipeline damage). The output blockage status variable logic is false, and a discharge leakage alarm is generated.

[0070] Output information on grain and oil raw materials during storage, including the actual remaining quality of raw materials, the cumulative number of seconds of storage risk, the remaining safe time, graded early warning signals, blockage status variables (logical true or logical false, with a blockage type label), the current average water activity value, and the current average temperature value, and record them to the intelligent management log of the grain and oil supply chain.

[0071] The tolerance value for mass conservation deviation is equal to the sum of the absolute value of the accuracy error of the total mass measuring sensor (pressure load cell) and the absolute value of the accuracy error of the cumulative outflow mass measuring sensor (Coriolis mass flow meter). The accuracy class of the pressure load cell is taken from the accuracy class and its corresponding maximum permissible error specified in the OIML 60 standard of the International Organization of Legal Metrology. The measurement accuracy of the Coriolis mass flow meter is taken from the accuracy class indicated on the flow meter's factory calibration certificate. The tolerance value for mass conservation deviation is equal to the sum of the maximum permissible error of the load cell at full scale and the maximum permissible error of the flow meter at full scale. The specific value is calculated from the nameplate parameters of the field equipment and the accuracy class in the calibration certificate.

[0072] The high threshold for safe time is equal to the standard processing time. If the remaining safe time is greater than the processing time of a full workday, there is sufficient time to complete the processing before the risk window is reached, even if a quality processing instruction is triggered now.

[0073] The minimum safe time threshold is equal to half the standard processing time. If the remaining safe time is less than half a business day, even if processing is initiated immediately, it may not be completed before the risk window is reached.

[0074] The continuous blockage judgment time window is equal to the physical transmission time from the start-up of the discharge conveyor to the actual discharge of material from the outlet. The calculation formula is: the total length of the conveyor (in meters) divided by the rated propulsion speed of the raw material in the equipment (in meters per second). The total length and rated propulsion speed are both taken from the equipment design drawings and factory test reports.

[0075] The stall current multiple of the motor is taken from the motor's factory type test report.

[0076] The specific process for determining the cumulative number of seconds of storage risk, the remaining safe time, and the tiered early warning signals is as follows:

[0077] For raw materials for which a risk timer is applicable, the initial risk accumulation time is used as the starting value. If the current second belongs to a toxic microenvironment, the risk accumulation time is increased by one; otherwise, the risk accumulation time remains unchanged. If the tightened judgment mark is yes, the judgment reference time is the tightened judgment reference time. If the tightened judgment mark is no, the judgment reference time is the lower limit of the physical lag time of toxic production.

[0078] Calculate the remaining safe time, which is equal to the judgment baseline time minus the current accumulated warehouse risk in seconds; if the remaining safe time is greater than the high threshold of safe time, no action is taken; if the remaining safe time is not less than the low threshold of safe time and not greater than the high threshold of safe time, a risk approach warning is generated.

[0079] If the remaining safe time is less than the low threshold of safe time, an urgent risk warning will be generated.

[0080] It needs to be explained that by performing mass conservation verification between pressure weighing and mass flow meters, the true inventory quality is ensured to be independent of material or liquid level readings, thus solving the problem of false volume traps caused by arching and wall adhesion. Continuous monitoring of multi-point moisture activity and temperature determines the microenvironment of toxicity risk based on spatial extreme values ​​rather than average values, avoiding the underreporting of localized hazardous areas due to averaging. Through eight combinations of cross-judgments based on three independent physical conditions, all equipment states, including mechanical blockage, false empty silo readings, motor overload, control loop anomalies, and discharge leakage, are covered. A residual evacuation time judgment is introduced after valve closure to prevent normal residual outflow from being falsely reported as leakage. The risk accumulation time is extended from the receiving stage to the storage stage, and a judgment benchmark time is determined by combining stricter judgment markers to calculate the remaining safety time and trigger tiered warnings accordingly. The above process transforms warehouse management from a crude model relying on level gauge readings and experience-based judgment to a deterministic management model based on multi-sensor cross-verification, risk quantification timing, and tiered warnings, simultaneously solving three types of problems: inaccurate inventory, underreporting of risks, and misjudgment of equipment status.

[0081] By combining the information on grain and oil raw materials entering the factory and the information on grain and oil raw materials during storage, a comprehensive inspection and processing diversion decision is made on the grain and oil raw materials before they leave the warehouse, and the characteristic data of grain and oil raw materials leaving the warehouse are obtained.

[0082] Obtain the blockage status variable; if the blockage status variable is logically false, continue to execute the subsequent steps; if the blockage status variable is logically true, check the blockage type label: if the label is mechanical blockage, generate a blockage clearing operation instruction, check the blockage status variable once per second until the logical false is restored, and then continue to execute the subsequent steps; if the label is empty warehouse misjudgment, automatically correct the actual remaining quality to zero, output the outbound permission signal logically false, and the process terminates.

[0083] Perform multi-dimensional hierarchical judgment of the quality status and output the outbound permission signal; if the outbound permission signal logic is true, read the actual remaining quality and compare the planned outbound quality (provided by the outbound request) with the actual remaining quality: if the planned outbound quality is less than or equal to the actual remaining quality, the final outbound quality is equal to the planned outbound quality; if the planned outbound quality is greater than the actual remaining quality, the final outbound quality is corrected to the actual remaining quality.

[0084] Calculate the remaining mass after delivery. The remaining mass after delivery is equal to the actual remaining mass minus the final delivery mass. If the remaining mass after delivery is less than the minimum effective operating capacity of the warehouse, generate a clearance suggestion.

[0085] Output grain and oil raw material outbound characteristic data, including outbound permission signal (logical true or logical false) and final outbound quality, and record it to the grain and oil supply chain intelligent management log.

[0086] For the tolerance of Class III accuracy of trade weighing instruments, please refer to the provisions on the accuracy classes of trade weighing instruments in the "Detailed Rules for the Implementation of the Metrology Law of the People's Republic of China".

[0087] The minimum effective operating capacity of a storage tank is determined by the effective volume (unit: cubic meters) indicated in the tank design documents (as-built drawings). It is set at 10% of the effective volume, derived from general design specifications in the petrochemical and grain and oil storage industries (such as the minimum liquid level requirements for storage tanks in GB50074 "Code for Design of Petroleum Depots") and economic considerations for tank cleaning operations.

[0088] like Figure 3 As shown, the specific process of performing multi-dimensional graded judgment of quality status and outputting the outbound permission signal is as follows: if the raw material type is marked as not applicable to the risk timer, skip the toxicity risk judgment and directly output the outbound permission signal logic as true; if the raw material type is marked as applicable to the risk timer, perform graded judgment.

[0089] If the remaining safe time is greater than the high threshold of safe time and the current average water activity value is less than the critical value of water activity for aflatoxin production, the system is deemed safe, and a release permission signal is output as true. If the remaining safe time is greater than the high threshold of safe time but the current average water activity value is not less than the critical value of water activity for aflatoxin production, the system is deemed safe, and a release permission signal is output as true with a suggestion for priority release. If the remaining safe time is not less than the low threshold of safe time and not greater than the high threshold of safe time, the system is deemed to be approaching a risk window, a priority release suggestion signal is generated, and a release permission signal is output as true. If the remaining safe time is less than the low threshold of safe time or the accumulated number of seconds of storage risk is greater than or equal to the judgment benchmark time, the system is deemed to have entered a risk window, and a release permission signal is output as false, and the process terminates.

[0090] In the blockage handling stage, two different physical causes—mechanical blockage and misjudgment of empty warehouses—are addressed with separate unblocking operations or inventory adjustments, ensuring that the false volume trap is ultimately identified and corrected during the outbound process. In the quality assessment stage, the remaining safety time and the current average moisture activity value are jointly used for grading, covering four states: safe, safe but requiring priority outbound, approaching the risk window, and already in the risk window. When approaching the risk window, a priority outbound suggestion signal is generated, automatically giving higher outbound priority to batches in a critical risk state without requiring manual review of quality records. In the outbound quantity verification stage, the planned outbound quantity is compared with the actual remaining mass to adjust the final outbound mass, and the remaining mass after outbound is calculated. When the remaining mass is lower than the minimum effective operating capacity of the warehouse, a clearing suggestion is generated, avoiding storage waste and mixing of new and old batches caused by small amounts of material occupying storage space for extended periods. This process transforms outbound decision-making from subjective judgment relying on manual record review to a rigid decision driven by three-dimensional physical data: blockage status, risk classification, and remaining inventory, improving storage capacity turnover efficiency while ensuring outbound quality safety.

[0091] Based on the outbound characteristic data of grain and oil raw materials, the loading and measurement of grain and oil raw materials are carried out and transportation is initiated, and the transportation information of grain and oil raw materials is output.

[0092] After the outbound operation begins, the Coriolis mass flow meter continuously measures the instantaneous mass flow rate and integrates it over time to obtain the cumulative outbound mass. When the cumulative outbound mass reaches the valve deceleration trigger ratio for the final outbound mass, a valve deceleration command is issued. When the final outbound mass is reached, a valve closing command is issued. After waiting for the residual mass to be emptied, the cumulative mass counter value is read and recorded as the shipment measurement mass. The shipment deviation is calculated, which is equal to the shipment measurement mass minus the final outbound mass.

[0093] If the absolute value of the shipping deviation is within the allowable tolerance range for grain or oil weighing, the shipping quality is equal to the measured shipping quality; if the shipping deviation exceeds the allowable tolerance range, a shipping deviation alarm signal is generated.

[0094] Obtain the transport vehicle identification number and summarize the shipment measurement mass, current storage risk accumulation seconds (reading at the end of shipment), current average moisture activity value, current average temperature value, shipment completion time, and transport vehicle identification number into a transport raw material data package.

[0095] When the transport vehicle leaves the factory area, the departure time of the raw material transport is recorded. From the departure time, the current longitude value, current latitude value and instantaneous ground speed value are output every second. If the instantaneous ground speed value remains zero within the continuous stop alarm time, a transport stop alarm signal is generated.

[0096] Output grain and oil raw material transportation information, including loading and weighing quality, transport raw material data package, raw material transportation departure time, current longitude value, current latitude value and instantaneous ground speed value, and record it to the grain and oil supply chain intelligent management log.

[0097] The tolerance range is calculated based on the maximum permissible error of Class III weighing instruments as specified in JJG 539-2016 "Digital Indicating Scales". The specific calculation method is: the final outgoing mass multiplied by the tolerance coefficient for the corresponding load range (e.g., if it falls within the range of 500e to 2000e, the coefficient is ±1.0e / m, where e is the verification scale division). This value is derived from the national metrological verification regulations and varies dynamically with the outgoing quantity.

[0098] The duration of continuous shutdown alarms is determined based on the delivery time limit stipulated in the transportation contract and the abnormal response plan.

[0099] The vehicle identification number (VIN) for road freight vehicles is the license plate number issued by the public security traffic management department; the VIN for waterway transport vessels is the vessel registration number issued by the maritime administration; and the VIN for railway freight cars is the car number assigned by the railway administration department.

[0100] Under the target outbound flow rate, different deceleration trigger ratios (e.g., 90%, 85%, 80%) are set in a stepwise manner for the valve deceleration trigger ratio. The cumulative excess outbound mass after the valve is fully closed is recorded each time. The acceptance criterion is that the excess outbound mass does not exceed the maximum permissible error of Class III trade weighing instrument (according to JJG 539-2016). The smallest trigger ratio within the acceptance range is selected, and after three consecutive repeated calibrations to verify stability, it is solidified and written into the enterprise standard operating procedure.

[0101] In one specific embodiment, the loading process utilizes mass flow meter integral measurement, valve-based graded deceleration control, and residual venting to solve the industry-wide challenge of accurately controlling loading volume. At the data flow level, loading measurement quality, accumulated storage risk seconds, moisture activity values, and temperature values ​​are aggregated into a non-editable transport raw material data package, ensuring that quality risk data is transmitted synchronously with the goods, providing a complete data starting point for arrival verification. At the transport monitoring level, latitude and longitude and instantaneous ground speed are continuously output from the departure time, and a stoppage alarm is automatically generated when continuous stoppages exceed the time limit, transforming the transport process from untraceable to real-time perceptible. This design transforms the loading process from a single weight transfer point into a key control node integrating precise measurement, quality data solidification, and transport process monitoring.

[0102] Based on the transportation information of grain and oil raw materials, the estimated arrival time in transit is calculated, and the arrival verification is carried out to determine the estimated arrival time, the quality of the goods, and the amount of transportation loss.

[0103] Based on the current longitude and latitude values, the spherical distance to the next necessary physical node is calculated as the remaining physical mileage.

[0104] Multiply the current longitude value by the constant pi and divide by 180 to get the current longitude in radians. Multiply the current latitude value by the constant pi and divide by 180 to get the current latitude in radians. Multiply the longitude value of the next necessary physical node by the constant pi and divide by 180 to get the longitude in radians of the next necessary physical node. Multiply the latitude value of the next necessary physical node by the constant pi and divide by 180 to get the latitude in radians of the next necessary physical node. 180 is the degree of a semicircle. Convert the four values ​​from angle units to radian units.

[0105] Subtract the current real-time latitude in radians from the latitude in radians of the next necessary physical node to obtain the latitude difference in radians; subtract the current real-time longitude in radians from the longitude in radians of the next necessary physical node to obtain the longitude difference in radians.

[0106] Divide the latitude difference in radians by two to get the latitude difference in half an arc. Calculate the sine of the latitude difference in half an arc and then multiply it by the latitude difference in half an arc to get the square of the sine of the latitude difference in half an arc.

[0107] Divide the longitude difference in radians by two to get the longitude difference in half an arc. Calculate the sine of the longitude difference in half an arc and then multiply it by the longitude difference in half an arc to get the square of the sine of the longitude difference in half an arc.

[0108] Calculate the cosine of the current real-time latitude radian value, calculate the cosine of the latitude radian value of the next necessary physical node, multiply the cosine of the current real-time latitude radian value and the cosine of the latitude radian value of the next necessary physical node by the square of the sine of the longitude difference half-arc, and finally add the product to the square of the sine of the latitude difference half-arc to obtain the intermediate variable value.

[0109] Calculate the square root of the intermediate variable value, calculate the arcsine of the square root of the intermediate variable value, multiply the arcsine of the square root of the intermediate variable value by two, and obtain the central angle in radians between the current point and the node.

[0110] Multiply the central angle in radians by the Earth's average radius (6,371 meters) to obtain the spherical distance between the current point and the next necessary physical node, which is recorded as the remaining physical mileage.

[0111] If the current real-time longitude value is exactly equal to the node's longitude value and the current real-time latitude value is exactly equal to the node's latitude value, then the latitude difference in radians and the longitude difference in radians are both zero, the intermediate variable is zero, the central angle is zero, the spherical distance is zero, and the remaining physical mileage is 0.

[0112] If the remaining physical mileage is less than 0.01 kilometer, the vehicle is determined to have reached the corresponding physical node, and the corresponding node is automatically marked as passed.

[0113] If the instantaneous ground speed is greater than zero, calculate the remaining travel time, which is equal to the remaining physical mileage divided by the instantaneous ground speed. If the instantaneous ground speed is equal to zero, mark it as stationary. When the instantaneous ground speed is last greater than zero, record the remaining physical mileage and the instantaneous ground speed at that time, calculate the remaining travel time at that time, and record it as the remaining travel time when the instantaneous ground speed is equal to zero. The remaining travel time at that time is equal to the remaining physical mileage divided by the instantaneous ground speed.

[0114] The estimated arrival time is equal to the current time plus the remaining travel time.

[0115] Record the estimated arrival time value every second from the departure time to the current time to form an estimated arrival time time series. Calculate the standard deviation of the time series as an indicator of the stability of the estimated arrival time. If the standard deviation of the time series is less than the estimated arrival time stability threshold, the estimated arrival time stability is marked as stable. If the standard deviation of the time series is greater than the estimated arrival time stability threshold, an estimated arrival time instability warning is generated.

[0116] The estimated arrival time window is calculated based on the average estimated arrival time over the last five minutes. The estimated arrival time window is then sent to the next necessary physical node to update the estimated arrival time window.

[0117] The specific calculation method for the estimated arrival time window is as follows: obtain all the estimated arrival times calculated in the last five minutes from the current time, and use them as the estimated arrival time time series dataset. The estimated arrival time series dataset contains three hundred estimated arrival time values ​​(one recorded per second).

[0118] Calculate the arithmetic mean of the estimated arrival time series dataset to obtain the average estimated arrival time over the most recent five minutes. Calculate the difference between each data point in the estimated arrival time series dataset and the average estimated arrival time over the most recent five minutes. Square each difference and sum them up. Divide the sum of squares by the total number of data points (300). Finally, take the square root of the quotient to obtain the standard deviation of the estimated arrival time over the most recent five minutes.

[0119] The lower limit of the estimated arrival time window is obtained by subtracting the standard deviation of the estimated arrival times over the past five minutes from the average of the estimated arrival times over the past five minutes. The upper limit of the estimated arrival time window is obtained by adding the standard deviation of the estimated arrival times over the past five minutes to the average of the estimated arrival times over the past five minutes. The lower and upper limits of the estimated arrival time window together form a closed interval, which is the estimated arrival time window.

[0120] After the raw materials arrive, weigh them upon arrival to output the arrival weight and calculate the transportation loss. The transportation loss is equal to the shipment weight minus the arrival weight.

[0121] The amount of transportation loss is compared with the upper limit of loss stipulated in the transportation contract. The upper limit of loss stipulated in the transportation contract is calculated by multiplying the loss rate stipulated in the transportation contract by the shipment weight. If the amount of transportation loss exceeds the upper limit of loss stipulated in the transportation contract, the excess loss is calculated. The excess loss is equal to the amount of transportation loss minus the upper limit of loss stipulated in the transportation contract. A claim form is automatically generated and filled in with the claim generation date, means of transport identification number, full name of the carrier corresponding to the means of transport identification number, shipment weight, arrival weight, amount of transportation loss, upper limit of loss stipulated in the transportation contract, excess loss, and compensation amount. The compensation amount is determined by reading the unit price of the contract for the corresponding batch of goods (in yuan per kilogram). The compensation amount is equal to the excess loss multiplied by the unit price of the goods contract.

[0122] Verify that the fields in the raw material transportation data package are consistent. If they are consistent, the data is complete; otherwise, generate a data integrity alarm.

[0123] The estimated arrival time, quality of goods, and amount of transportation losses will be recorded in the intelligent management log of the grain and oil supply chain.

[0124] The estimated arrival time stability threshold is adjusted by the company's transportation dispatch department based on transportation distance, road conditions, and the receiving party's operational capabilities. For example, ±30 minutes is used for short-distance transportation, and ±2 hours for long-distance transportation.

[0125] In one specific embodiment, the remaining physical mileage is calculated using the spherical distance formula based on real-time latitude and longitude and physical node coordinates. This ensures that the estimated arrival time calculation is not dependent on the empirical predictions of third-party map services, but is entirely based on deterministic geometric and physical relationships. When the local speed reaches zero, the system automatically switches to a standstill replacement algorithm to avoid invalid jumps in estimated arrival times caused by temporary vehicle stops. By continuously recording the estimated arrival time series and calculating the standard deviation, stability markers and instability warnings are generated, allowing the recipient to understand the degree of uncertainty when the estimated arrival time fluctuates drastically. Simultaneously, an arrival time window is constructed using the mean and standard deviation of the estimated arrival time data from the most recent five minutes, replacing a single instantaneous estimated arrival time value, providing a more robust time reference for unloading scheduling. Upon arrival, the transportation loss is calculated by comparing the loading quality with the arrival quality, and a claim form containing details of loss and compensation amount is automatically generated when the contractually agreed limit is exceeded. Combined with field consistency verification of the transport raw material data package, it is ensured that the data used for settlement has not been tampered with since shipment. The above process upgrades the arrival verification from a manual document comparison mode to a deterministic process with automatic loss calculation, automatic claim generation, and automatic data integrity verification. This eliminates settlement disputes caused by missing documents, inconsistent data, or discrepancies in loss calculation. At the same time, it enables the receiving party to reasonably schedule unloading resources based on the stability marker of the expected arrival time and the arrival time window, avoiding equipment idleness or production scheduling chaos caused by inaccurate expected arrival time.

[0126] Reference Figure 2 As shown, the second aspect of the present invention provides an intelligent management system for the grain and oil supply chain, including: a raw material warehousing processing module, a raw material storage analysis module, a raw material outbound inspection module, a raw material transportation analysis module, and a raw material transportation verification module.

[0127] The raw material warehousing processing module is used to physically weigh and initialize the risk of grain and oil raw materials arriving at the factory, determine the warehousing permit and divert the raw materials, and determine the warehousing information of the grain and oil raw materials arriving at the factory.

[0128] The raw material storage analysis module is used to weigh the inventory and continuously time the risk during the storage period based on the information of grain and oil raw materials entering the factory, and output the information of grain and oil raw materials during the storage period.

[0129] The raw material outbound inspection module is used to combine the information on grain and oil raw materials entering the factory and the information on grain and oil raw materials during storage to perform comprehensive inspection and processing diversion decisions on grain and oil raw materials before they leave the warehouse, and obtain characteristic data of grain and oil raw materials leaving the warehouse.

[0130] The raw material transportation analysis module is used to measure the loading and transportation of grain and oil raw materials based on the outbound characteristic data of grain and oil raw materials, and output the transportation information of grain and oil raw materials.

[0131] The raw material transportation verification module is used to calculate the estimated arrival time in transit based on the transportation information of grain and oil raw materials, and to verify the arrival time, quality of goods, and amount of transportation loss.

[0132] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A method for intelligent management of the grain and oil supply chain, characterized in that, Includes the following steps: Physically weigh and initialize the risk of grain and oil raw materials arriving at the factory, determine the warehousing permit and divert the raw materials to the factory, and determine the warehousing information of the grain and oil raw materials arriving at the factory. Based on the information on grain and oil raw materials entering the warehouse, inventory weighing and risk tracking are performed during the storage period, and the information on grain and oil raw materials during the storage period is output. By combining the information on grain and oil raw materials entering the factory and the information on grain and oil raw materials during storage, a comprehensive inspection and processing diversion decision is made on the grain and oil raw materials before they leave the warehouse, and the characteristic data of grain and oil raw materials leaving the warehouse is obtained. Based on the outbound characteristic data of grain and oil raw materials, the loading and measurement of grain and oil raw materials are carried out and transportation is initiated, and the transportation information of grain and oil raw materials is output. Based on the transportation information of grain and oil raw materials, the estimated arrival time in transit is calculated, and the arrival verification is carried out to determine the estimated arrival time, the quality of the goods, and the amount of transportation loss.

2. The intelligent management method for the grain and oil supply chain according to claim 1, characterized in that, The specific process for physically weighing and risk initialization of grain and oil raw materials arriving at the factory is as follows: Obtain the total mass of raw materials upon entry into the warehouse; Obtain the water activity and temperature values ​​at the time of raw material unloading; A pulse signal is generated every second. At the rising edge of each pulse, the real-time water activity and temperature values ​​of the raw material are acquired. It is determined whether the water activity value of the raw material is greater than the critical value of water activity for aflatoxin production and whether the temperature value of the raw material is greater than the lower limit of temperature for aflatoxin production. If both conditions are met, the initial risk accumulation time is increased by one. If either condition is not met, the initial risk accumulation time remains unchanged and the initial risk accumulation time is output.

3. The intelligent management method for the grain and oil supply chain according to claim 2, characterized in that, The specific process for determining the entry permit and sorting of raw grains and oils into the factory warehouse is as follows: Retrieve the raw material name field and water activity value; If the raw material belongs to the category of grains or oilseeds and its water activity value is greater than the water activity limit of refined oil, the raw material type will be marked as applicable risk timer. If the raw material belongs to the category of refined oils and the water activity value is less than or equal to the water activity limit of refined oils, the raw material type is marked as not applicable to the risk timer. If the raw material belongs to the category of grains or oilseeds and its water activity value is less than or equal to the water activity limit of refined oils, the raw material type is marked as applicable risk timer. If the raw material belongs to the category of refined oils and its water activity value is greater than the water activity limit of refined oils, the raw material type will be marked as applicable risk timer. For raw materials that are not subject to risk timers, output directly and mark the warehousing permission signal logic as true; Execution risk assessment logic: For raw materials for which a risk timer is applicable, read the historical cumulative risk seconds of the upstream supply chain of the corresponding raw material, calculate the total cumulative risk seconds, and compare the total cumulative risk seconds with the lower limit of the physical lag time for toxicity production. If the total risk accumulation in seconds is less than the lower limit of the physical lag time for toxic production, the corresponding raw material warehousing permission signal logic is output as true, and the tightened judgment flag is set to no. If the total accumulated risk in seconds is greater than or equal to the lower limit of the physical lag time for toxicity production, calculate the risk over-limit multiple. The risk over-limit multiple is equal to the integer part of the quotient obtained by dividing the total accumulated risk in seconds by the lower limit of the physical lag time for toxicity production. If the risk over-limit multiple is equal to 1, output the warehousing permission signal logic as false and generate a first-level forced physical processing instruction. If the risk over-limit multiple is greater than 1, output the warehousing permission signal logic as false and generate a quality scrap instruction. After the forced physical processing instruction is executed, update the water activity and temperature values ​​of the corresponding raw materials after processing, calculate the total cumulative risk seconds, and execute the risk judgment logic again. If the result is that the total risk accumulation in seconds is less than the lower limit of the physical lag time for toxic production, the tightened judgment flag will be set to yes, the logic of the warehousing permission signal will be set to true, and the corresponding raw material outbound judgment benchmark time will be forcibly set to the tightened judgment benchmark time. If the result is that the total risk accumulation in seconds is greater than or equal to the lower limit of the physical lag time for toxic production, the risk exceedance multiple is recalculated. If the risk exceedance multiple is 1, obtain the number of times the processing has been performed. If the number of processing is less than 2, output the warehousing permission signal logic as false and generate a first-level forced physical processing instruction. If the number of processing has reached 2, output the warehousing permission signal logic as false and generate a quality scrap instruction. If the risk exceeds the limit by 2, output the warehousing permission signal as false and generate a quality scrap instruction. The system outputs the warehouse entry permission signal, tightened judgment mark, raw material type mark, total raw material warehouse entry mass, initial risk accumulation seconds, water activity value, and temperature value as the information on grain and oil raw materials entering the warehouse at the factory.

4. The intelligent management method for the grain and oil supply chain according to claim 1, characterized in that, The specific process for weighing inventory and continuously timing risks during warehousing, and outputting information on grain and oil raw materials during warehousing, is as follows: Obtain the true remaining mass of the raw materials; The difference between the actual remaining mass of raw materials in the previous second and the actual remaining mass of raw materials in the current second is recorded as the actual remaining mass difference. The cumulative outflow mass increment between the previous second and the current second is recorded as the outflow mass increment difference. If the difference between the actual remaining mass difference and the outflow mass increment difference exceeds the mass conservation deviation tolerance value, a sensor consistency alarm is generated, and the inventory data is marked as pending calibration. Every second, the water activity and temperature values ​​of all measuring points are read. If the water activity value of any measuring point is greater than the critical value of water activity for Aspergillus flavus toxin production and the temperature value is greater than the lower limit of temperature for Aspergillus flavus toxin production, the current second is determined to be a microenvironment with a risk of toxin production, the corresponding measuring point is recorded as a risk measuring point, and the risk area coverage rate is calculated. Obtain the current average water activity value and current average temperature value for all measuring points; Determine the cumulative number of seconds of warehouse risk, the remaining safe time, and the tiered early warning signals; Every second, cross-check the execution conditions and output the blockage status variable logic and the corresponding blockage type label based on the truth or falsehood of the execution conditions. The blockage status variable logic includes true and false, and the blockage type label includes mechanical blockage, empty warehouse misjudgment, motor overload warning signal, normal continuation of outbound process, control loop abnormal alarm, no operation and material leakage alarm. The data output includes the actual remaining mass of raw materials, the cumulative number of seconds of storage risk, the remaining safe time, the graded early warning signals, the blockage status variables, the current average water activity value, and the current average temperature value, which serve as information on grain and oil raw materials during storage.

5. The intelligent management method for the grain and oil supply chain according to claim 4, characterized in that, The specific process for determining the cumulative number of seconds of storage risk, the remaining safe time, and the tiered early warning signals is as follows: For raw materials for which a risk timer is applicable, the initial risk accumulation time is used as the starting value. If the current second belongs to a toxic microenvironment, the risk accumulation time is increased by one; otherwise, the risk accumulation time remains unchanged. If the tightening judgment mark is set to "Yes", the judgment reference time is the tightening judgment reference time. If the stricter judgment mark is no, the judgment benchmark time is the lower limit of the physical lag time of toxin production; Calculate the remaining safe time, which is equal to the judgment baseline time minus the current accumulated number of seconds of warehouse risk. If the remaining safe time is greater than the high threshold of the safe time, no operation is performed. If the remaining safe time is not less than the lower threshold of safe time and not greater than the upper threshold of safe time, a risk approach warning is generated. If the remaining safe time is less than the low threshold of safe time, an urgent risk warning will be generated.

6. The intelligent management method for the grain and oil supply chain according to claim 1, characterized in that, The specific process for implementing comprehensive pre-outbound inspection and processing diversion decisions for grain and oil raw materials, and obtaining outbound characteristic data of grain and oil raw materials, is as follows: Perform multi-dimensional hierarchical judgment of quality status and output outbound permission signal; If the outbound permission signal logic is true, read the actual remaining quality and compare the planned outbound quality with the actual remaining quality: If the planned outbound quality is less than or equal to the actual remaining quality, the final outbound quality equals the planned outbound quality. If the planned outbound quality is greater than the actual remaining quality, the final outbound quality will be adjusted to the actual remaining quality. The outbound permission signal and the final outbound quality are output as grain and oil raw material outbound characteristic data.

7. The intelligent management method for the grain and oil supply chain according to claim 6, characterized in that, The specific process of performing multi-dimensional graded judgment of quality status and outputting outbound permission signal is as follows: If the raw material type is marked as not applicable to the risk timer, skip the toxicity risk assessment and directly output the logic of the outbound permission signal as true. If the raw material type is marked as applicable to the risk timer, perform a classification determination: If the remaining safe time is greater than the high threshold of safe time and the current average water activity value is less than the critical value of water activity for aflatoxin production, it is determined to be safe, and the logic of outputting the release permission signal is true. If the remaining safe time is greater than the high threshold of safe time but the current average water activity value is not less than the critical value of water activity for aflatoxin production, it is determined to be safe, and the logic of outputting the outbound permission signal is true with the suggestion to prioritize outbound remarks. If the remaining safe time is not less than the lower threshold of safe time and not greater than the higher threshold of safe time, it is determined to be close to the risk window, a priority outbound suggestion signal is generated, and the outbound permission signal is output as true. If the remaining safe time is less than the low threshold of safe time, it is determined that the risk window has been entered, the logic of outputting the outbound permission signal is false, and the process is terminated.

8. The intelligent management method for the grain and oil supply chain according to claim 1, characterized in that, The specific process of loading, measuring, and initiating transportation of grain and oil raw materials, and outputting transportation information for these raw materials is as follows: The instantaneous mass flow rate is continuously measured, and the cumulative outflow mass is obtained by integrating over time. When the cumulative outflow mass reaches the valve deceleration trigger ratio of the final outflow mass, a valve deceleration command is issued. When the final outflow mass is reached, a valve closing command is issued. After waiting for the residual to be emptied, the value of the cumulative mass counter is read and recorded as the shipment metering mass. Calculate the shipping deviation, which is equal to the measured shipment weight minus the final outbound weight; If the absolute value of the shipment deviation is within the allowable tolerance of grain weighing or oil weighing, the shipment quality is equal to the shipment measurement quality. If the shipment deviation exceeds the allowable range, a shipment deviation alarm signal will be generated. Obtain the transport vehicle identification number and summarize the shipment measurement mass, current storage risk accumulation in seconds, current average moisture activity value, current average temperature value, shipment completion time, and transport vehicle identification number into a transport raw material data package; Record the departure time of raw material transportation, and output the current longitude value, current latitude value and instantaneous ground speed value every second from the departure time. If the instantaneous ground speed value remains zero within the continuous stagnation alarm time, a transportation stagnation alarm signal is generated. The system outputs the shipment measurement mass, transport raw material data package, raw material transport departure time, current longitude value, current latitude value, and instantaneous ground speed value as grain and oil raw material transport information.

9. The intelligent management method for the grain and oil supply chain according to claim 1, characterized in that, The specific process for calculating the estimated arrival time in transit, verifying the goods upon arrival, and determining the estimated arrival time, quality of goods, and amount of transportation loss is as follows: Based on the current longitude and latitude values, calculate the spherical distance to the next necessary physical node as the remaining physical mileage; If the instantaneous ground speed is greater than zero, the remaining travel time is calculated. The remaining travel time is equal to the remaining physical mileage divided by the instantaneous ground speed. If the instantaneous ground speed value is equal to zero, it is marked as stationary. When the instantaneous ground speed value is greater than zero for the last time, the remaining physical mileage and the instantaneous ground speed value at that time are recorded. The remaining travel time at that time is calculated and recorded as the remaining travel time when the instantaneous ground speed value is equal to zero. The remaining travel time at that time is equal to the remaining physical mileage at that time divided by the instantaneous ground speed value at that time. The estimated time of arrival equals the current time plus the remaining travel time; Record the estimated arrival time value for each second from the departure time to the current time to form an estimated arrival time time series, and calculate the standard deviation of the time series as an indicator of the stability of the estimated arrival time; If the standard deviation of the time series is less than the stability threshold of the expected arrival time, the stability of the expected arrival time is marked as stable; If the standard deviation of the time series is greater than the stability threshold of the expected arrival time, an instability warning for the expected arrival time is generated. The estimated arrival time window is calculated based on the average estimated arrival time over the last five minutes. The estimated arrival time window is then sent to the next necessary physical node to update the estimated arrival time window. After the raw materials arrive, the arrival weight is output and the transportation loss is calculated. The transportation loss is equal to the shipment weight minus the arrival weight. Verify that the fields in the raw material transportation data package are consistent. If they are consistent, the data is complete; otherwise, generate a data integrity alarm.

10. A grain and oil supply chain intelligent management system, applied to the grain and oil supply chain intelligent management method according to any one of claims 1-9, characterized in that, include: The raw material warehousing processing module is used to physically weigh and initialize the risk of grain and oil raw materials arriving at the factory, determine the warehousing permit and diversion process, and determine the warehousing information of grain and oil raw materials arriving at the factory. The raw material storage analysis module is used to weigh the inventory and continuously time the risk during the storage period based on the information of grain and oil raw materials entering the factory, and output the information of grain and oil raw materials during the storage period. The raw material outbound inspection module is used to combine the information on grain and oil raw materials entering the factory and the information on grain and oil raw materials during storage to perform comprehensive inspection and processing diversion decisions on grain and oil raw materials before they leave the warehouse, and to obtain characteristic data of grain and oil raw materials leaving the warehouse. The raw material transportation analysis module is used to measure the loading and transportation of grain and oil raw materials based on the outbound characteristic data of grain and oil raw materials, and to output the transportation information of grain and oil raw materials. The raw material transportation verification module is used to calculate the estimated arrival time in transit based on the transportation information of grain and oil raw materials, and to verify the arrival time, quality of goods, and amount of transportation loss.

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