An automatic grain sample storage method for docking intelligent sampling system

Abstract

The application discloses an automatic grain sample storage method for a docking intelligent sampling system, and relates to the technical field of intelligent sampling; after the intelligent sampling equipment completes automatic sampling of the incoming train grain sample, the sample is transported to a sample receiving position of a sample storage line body, and the basic information of the sample is acquired; by collecting sample storage line body operation information and constructing an operation intensity curve, it is determined whether the current state is a task intensive state, a near-end candidate flow sequence to the storage library is generated based on the current node connection relationship in the task intensive state, and the optimal flow sequence is screened through the node congestion coefficient, the predicted completion time and the node switching frequency, so that the newly sampled sample can be quickly and orderly stored in the library; after the sample completes the initial storage, on-demand re-inspection is performed according to the sample number, and the sample bottle state is written back through the weighing module; then, the samples in the storage library are stored and evaluated, the actual retention period of the samples is dynamically determined, and after the expiration, the waste material and empty bottles are automatically recycled.

Description

Technical Field

[0001] This invention relates to the field of intelligent sampling technology, and in particular to an automated method for storing grain samples that is connected to an intelligent sampling system. Background Technology

[0002] During the grain procurement, storage, processing, and quality supervision processes, it is typically necessary to sample and test incoming grain to determine whether the moisture content, impurities, defective grains, mycotoxins, and other quality indicators meet the requirements. To meet the needs of quality verification, dispute arbitration, random inspections, and quality traceability, the samples taken after testing must be retained and properly stored for a specified period for subsequent retesting. Therefore, sample retention management is an important component of the grain quality testing system.

[0003] With the development of intelligent sampling technology, more and more grain storage enterprises are adopting automated sampling equipment to automatically sample incoming grain at multiple points, and using intelligent sampling systems to automatically collect sample information and manage the testing process. However, most existing technologies only automate the sampling process, while the sample retention stage still generally relies on manual management. For example, after sampling, manual tasks such as bottling, labeling, registration, transfer, and warehousing are usually required. When samples need to be retested, manual location of the corresponding sample bottle and sampling are still required. After the retention period expires, manual verification of the retention period and disposal of the sample are still necessary. These methods are not only inefficient but also prone to problems such as misplacement, incorrect sampling, missed registration, and incomplete traceability information. Summary of the Invention

[0004] Therefore, it is necessary to provide an automated grain sample retention and storage method that connects to an intelligent sampling system to address the problems mentioned in the background technology.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] An automated method for grain sample retention and storage that interfaces with an intelligent sampling system includes:

[0007] Step S100: After the automatic sampling of the grain sample from the incoming vehicle is completed, the automated sampling equipment transports the sample to the receiving position of the sampling line and acquires the basic information of the sample. At the same time, the operation information on the sampling line is collected according to a preset cycle, and the operation density corresponding to each collection time is calculated based on the operation information. The operation density curve is plotted. When the operation density curve meets the preset density judgment condition, it is determined that the current task is in a dense state.

[0008] Step S200: When the current task is not in a high-intensity state, the sample transfer to the storage is completed in a preset order; when the current task is in a high-intensity state, several near-end candidate transfer sequences to the storage are generated based on the node connection relationship between the sample retention line and the storage, and the candidate transfer sequences are screened step by step to determine the optimal transfer sequence and then the sequence is sent out for execution.

[0009] In step S300, after the sample is initially stored, the corresponding sample bottle is called according to the sample number to perform on-demand re-inspection, and the status of the sample bottle is written back according to the change in bottle weight before and after the re-inspection. The storage evaluation of each sample in the storage room is carried out, and the retention period of the sample is dynamically determined based on the evaluation results.

[0010] In some embodiments, the sampling locations include at least two or more of the following: front, middle, and rear sampling locations along the length of the grain truck compartment; left, middle, and right sampling locations along the lateral direction; and upper, middle, and lower sampling locations along the height direction; the sampling locations also include sampling locations in the four corner areas and the center area of ​​the truck compartment.

[0011] In some embodiments, the operation information includes at least the number of samples to be processed, the number of arriving samples, the remaining empty bottles in the sample retention line, and the number of samples to be transported at each collection time. Based on these, the processing pressure coefficient, arrival impact coefficient, empty bottle shortage coefficient, and transport retention coefficient are obtained respectively. Then, the operation density is calculated by hierarchically fusing the processing pressure coefficient, arrival impact coefficient, transport retention coefficient, and empty bottle shortage coefficient. Specifically, this includes:

[0012] Pre-set corresponding reference upper limits include: the upper limit of the acceptable number of samples to be processed under normal operating conditions of the sampling line, the upper limit of the acceptable number of arriving samples within a unit collection cycle, the upper limit of empty bottle remaining capacity, and the upper limit of the number of samples to be transported. Based on these reference upper limits, the number of samples to be processed, the number of arriving samples, the empty bottle remaining capacity of the sampling line, and the number of samples to be transported are standardized to obtain four dimensionless coefficients: the processing pressure coefficient, the arrival impact coefficient, the empty bottle shortage coefficient, and the transport retention coefficient, which are then labeled as follows: , , and Where t is the index of the acquisition time; through (1- The remaining capacity of the unsaturated number of samples to be treated was calculated, (1- The remaining capacity before congestion occurs at the sample input is calculated, and the product of the two values ​​and the inverse is taken to obtain the sample inlet pressure index. This represents the current sampling pressure level; the specific calculation formula is:

[0013]

[0014] It should be noted that when the number of samples to be processed is high, even if the number of newly arriving samples is not large, the sample receiving pressure will still remain at a high level; when the number of samples to be processed is not high but the number of newly arriving samples suddenly increases, the sample receiving pressure will also rise rapidly, thereby avoiding the passivation problem caused by simple linear addition.

[0015] Through (1+ The amplification effect of the current sample receiving pressure on the downstream transfer process is calculated, and then multiplied by the transfer retention coefficient. The transit and retention index was obtained. The range of the transit retention index is limited by min(·, 1), and the specific calculation formula is as follows:

[0016]

[0017] It should be noted that when the sample receiving pressure is high, the samples that have not yet been transferred out of the sample retention line are more likely to accumulate at the rear end, thus the flow retention index will be further amplified; when the sample receiving pressure is low, the amplification effect of the samples to be transferred on the overall congestion is smaller.

[0018] Sample pressure index Transshipment and Circulation Detention Index And empty bottle shortage coefficient By performing hierarchical fusion, the task intensity can be obtained. The specific calculation formula is as follows:

[0019]

[0020] It should be noted that the intensity of the work The value ranges from 0 to 1. The closer the value is to 1, the more intensive the current operation is. The sampling pressure, circulation delay and resource shortage work together to affect the final intensity according to the hierarchy of front-end sampling pressure, back-end transfer delay and insufficient bottle space resources. The operation intensity will be low only when all three indicators are low. As long as any one of them increases significantly, the overall intensity will be rapidly increased.

[0021] In some embodiments, extreme points of the work intensity curve are identified, and least-squares linear fitting is performed on the analysis segment between adjacent extreme points to obtain the trend of change of the analysis segment, i.e., the slope. The number of collection points within the analysis segment whose work intensity is greater than or equal to a preset intensity threshold is counted. When the slope of the analysis segment is greater than the slope threshold or the number of collection points reaches a quantity threshold, the analysis segment is recorded as a valid segment. Specifically, this includes:

[0022] The least squares linear fitting method is used to fit the work density corresponding to each collection time in the analysis segment, resulting in a fitted straight line for the analysis segment. The slope of the fitted straight line represents the trend value of the analysis segment, and the slope characterizes the rate of change of work density between two adjacent extreme points. The larger the value, the steeper the rise or fall of the curve in the segment. The number of collection points in the analysis segment with work density greater than a preset density threshold is counted. The preset density threshold is used to distinguish between normal fluctuation state and intensive work state, and is set to 0.7 in this embodiment. The average slope of the sample line body in the analysis segment during non-intensive work period is taken and multiplied by an amplification factor to obtain the slope threshold. In this embodiment, the amplification factor is set to 1.5 to make the slope threshold higher than the normal fluctuation level. The number of collection points in the analysis segment is multiplied by a preset percentage and rounded up to obtain the quantity threshold. In this embodiment, the percentage is set to 60%, indicating that at least 60% of the collection points in the analysis segment are above the density threshold before the analysis segment is considered to have obvious intensive characteristics. The analysis segment is recorded as a valid segment when it meets any of the following conditions.

[0023] In some embodiments, the operation density curve satisfies the preset density judgment condition as follows: A summary analysis is performed on all effective segments, the sum of the durations of all effective segments is calculated to obtain the dense duration, the total duration corresponding to the operation density curve is calculated, and the ratio of the dense duration to the total duration is used as the dense coverage rate; simultaneously, the over-threshold area formed between the operation density curve and the preset density threshold within each effective segment is calculated, and the over-threshold areas corresponding to all effective segments are accumulated to obtain the global over-threshold area, and the dense over-intensity is calculated based on the global over-threshold area; when the dense coverage rate is greater than or equal to the first judgment threshold, or the dense over-intensity is greater than or equal to the second judgment threshold, the operation density curve is determined to satisfy the preset density judgment condition; wherein, the first judgment threshold is set to 0.5, indicating that when more than half of the analysis window time falls within the effective segment, it is considered a dense operation environment; the second judgment threshold is set to 1, indicating that when the cumulative area of ​​the operation density exceeding the threshold reaches the preset standard, it is considered a dense operation environment; specifically, the dense over-intensity calculation formula is:

[0024]

[0025] Where S represents the global overthreshold area, and T represents the total duration of the curve. The density threshold is represented by R; the density superintensity R represents the cumulative intensity of the work density exceeding the preset threshold throughout the curve.

[0026] In some embodiments, under task-intensive conditions, the flow process from the sample line to the storage is abstracted as a directed connection graph, and a number of proximal candidate flow sequences are generated using breadth-first search.

[0027] In some embodiments, the screening of candidate flow sequences includes: obtaining the number of tasks currently waiting to be processed for each node in the sequence and the designed processing limit of the node, calculating the node congestion ratio, and taking the maximum value of the node congestion ratio as the bottleneck congestion coefficient; removing the corresponding candidate flow sequence when the bottleneck congestion coefficient is greater than or equal to the bottleneck threshold; further calculating the sum of the node action time and the transfer time between nodes to obtain the expected completion time for the retained candidate flow sequences, and determining the optimal flow sequence in the order of minimum bottleneck congestion coefficient, minimum expected completion time, and minimum number of node switching.

[0028] In some embodiments, after determining the optimal flow sequence, the key nodes and connection channels in the optimal flow sequence are temporarily locked, and the lock is released after the current node completes its action, so as to prevent other sample tasks from occupying the same node or channel at the same time.

[0029] In some embodiments, during re-inspection, the sampling platform automatically opens the sample bottle for the inspector to take a sample, and after the inspector has taken the sample, the sampling platform automatically closes the bottle. The weight difference of the bottle before and after re-inspection is obtained through the weighing module. Based on the weight difference and a preset threshold, the sample bottle is determined to be full, half or empty, and the determination result is written back to the chip on the bottle.

[0030] In some embodiments, the sample number, subsample number, grain variety, initial storage time, current storage duration, basic retention period, and inspection records of each subsample are obtained. The inspection records include the inspection results of each subsample. Based on the inspection results, the number of re-inspections and the number of abnormal records for all subsamples are counted. If both the number of re-inspections (n) and the number of abnormal records (m) for a sample are zero, the basic retention period is directly compared with the current storage duration to determine whether to continue storage or discard the sample. Otherwise, a discrete inspection value is assigned to each subsample based on its inspection results. A position weight is set according to the sampling location of each subsample. A subsample diffusion factor is calculated based on the discrete inspection value and position weight. The subsample diffusion factor is then combined with the number of re-inspections and the number of abnormal records to calculate the sample retention value index. Based on this, the actual retention period of the sample is dynamically determined. Upon expiration, the sample is discarded and empty bottles are recycled. Specific implementations include:

[0031] Suppose that the sample from the same grain truck contains K sub-samples, and the discrete inspection value of the inspection result of the i-th sub-sample is denoted as . , where i represents the index of the subsample; when there is no re-inspection and no abnormality, the discrete check value is... =0, when there is a re-inspection and no abnormality, then the discrete check value is 0. =0.5; when an anomaly exists and there is no re-inspection, the discrete check value is... =0.7; when both re-inspection and abnormality exist simultaneously, the discrete inspection value is... =1; The position weight is set according to the sampling location of each subsample, denoted as . Discrete test values ​​of the test results for each sample The sample diffusion factor d is calculated using the location weights, and the formula is as follows:

[0032]

[0033] It should be noted that the subsample diffusion factor is used to characterize the degree of diffusion of abnormal or re-examination states at different sampling locations. When multiple high-weight locations correspond to... When the value is large, it indicates that there is a significant risk diffusion of the sample across the entire vehicle's spatial distribution;

[0034] The sample value index V is calculated using the following formula: The sample diffusion factor d, the number of re-inspections n, and the number of abnormal records m.

[0035]

[0036] The sample retention value index is used to characterize the necessity of retaining a sample. A higher value indicates greater potential value for the sample in subsequent quality traceability, dispute review, and risk assessment, thus making continued retention more necessary. Specifically, value compensation is applied to samples with re-inspection needs or abnormal risks. To prevent an excessively high index from leading to indefinite extension of the retention period, an upper limit is imposed on the sample retention value index, resulting in a corrected effective value index. , specifically: According to the revised effective value index Dynamically determine the actual retention period The expression is: ,in The base retention period is defined as λ, which is the extension period correction coefficient used to control the extension range. In this embodiment, the value is 0.5. When the current storage time is less than the actual retention period, the sample continues to be retained. When the current storage time is equal to or greater than the actual retention period, the sample is discarded. After the discard task is generated, the empty bottles are recycled. It should be noted that in the empty bottle recycling process, the empty bottles are cleaned, dried, and cleared of status in sequence. The sample information in the bottle chip is deleted or archived, and only the necessary historical traceability records are retained. The empty bottle is remarked as an empty bottle and returned to the empty bottle area of ​​the storage warehouse. When the remaining empty bottles in the sample retention line are lower than the preset replenishment threshold, they are retrieved from the empty bottle area of ​​the storage warehouse and replenished to the waiting position of the sample retention line so as to enter the next round of automatic sampling and retention process.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] 1. By refining the sampling locations into multiple positions such as the front, middle, rear, left and right sides, upper and lower layers, as well as the four corners and center of the car, and managing multiple sub-samples obtained from different locations of the same grain car as a single sample, and constructing an operation density curve by combining the number of samples to be processed, the number of arriving samples, the remaining empty bottles, and the number of samples to be transferred, the working status of the sampling retention line can be analyzed based on this, enabling real-time, continuous, and quantitative judgment of the busyness of the sampling retention line. It can accurately identify whether the sample has entered a dense operation environment as soon as it enters the sampling retention line, thus providing a basis for whether to use the near-end candidate path screening in the future, reducing the problems of missed samples, wrong samples, and insufficient empty bottles caused by the congestion of the sampling retention line, while improving the representativeness and traceability of sub-samples from multiple locations;

[0039] 2. When tasks are intensive, instead of using a fixed sequence for simple direct access, several candidate flow sequences to the storage warehouse are first generated based on the current node connection relationship. Then, the optimal sequence is selected step by step through node congestion coefficient, estimated completion time, and number of node switching. The optimal sequence is executed sequentially through node and channel locking mechanism. This enables orderly scheduling among the storage warehouse, same-floor transfer equipment, cross-floor transfer equipment and bottle picking and placing equipment, prioritizing the avoidance of bottleneck nodes and high-congestion channels. This ensures that new samples can be quickly, stably and accurately stored even in intensive conditions, avoiding sample retention, equipment conflicts or flow blockages caused by improper path selection.

[0040] 3. Conduct on-demand re-inspection, weighing and rewriting, dynamic retention period determination, and waste recycling management for samples already in storage: During re-inspection, the sample is taken by opening the lid through the sampling platform, and the bottle is then weighed to determine whether it is full, half-full, or empty, and the chip is rewritten; for samples in the storage, the retention value is assessed by combining the sample number, subsample number, grain variety, initial storage time, basic retention period, number of re-inspections, number of abnormal records, and subsample diffusion factor, and the retention period is dynamically determined accordingly. After the expiration, waste and empty bottles are recycled, changing the determination of sample storage from a fixed period to a dynamic period. The re-inspection needs, abnormal risks, and subsample location diffusion are included in the unified storage evaluation, so that high-risk samples can obtain a longer retention period, while ordinary samples can be removed on time, taking into account the needs of quality traceability, dispute verification, and storage turnover efficiency. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0043] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0044] Application Scenarios: This invention is applicable to grain enterprises that have already built automated sampling equipment and have needs for sample retention and re-inspection management. It is especially suitable for scenarios where the sampling equipment and the inspection area are not on the same floor or in the same room. In this embodiment, the method is completed collaboratively by a sample retention control server, automated sampling equipment, sample retention line, bottle picking and placing equipment, cross-floor transfer equipment, same-floor transfer equipment, sampling platform, storage warehouse, and waste disposal equipment. It realizes the fully automated management of the entire process of sample collection, from sampling, bottling, capping, storage, transfer, re-inspection, waste disposal to empty bottle recycling, avoiding the problems of low efficiency, high error rate, and incomplete traceability caused by manual carrying, manual registration, and manual bottle replenishment.

[0045] like Figure 1 As shown, an automated grain sample retention and storage method that connects to an intelligent sampling system includes the following steps:

[0046] Step 100: After the intelligent sampling system completes the automatic sampling of the grain sample from the incoming vehicle, the automated sampling equipment transports the sample to the receiving position on the sampling line and simultaneously acquires the basic information of the sample. This basic information includes at least the sampling time, grain variety, vehicle license plate number, sample number, subsample number, and sampling location. It should be noted that in this embodiment, the sampling location includes at least two or more of the following: sampling locations along the length of the grain truck's cargo compartment (front, middle, and rear); sampling locations along the lateral direction (left, middle, and right); and sampling locations along the height direction (upper, middle, and lower). For vehicles with uneven loading or significant differences in cone shape, this further includes sampling locations in the four corner areas and the center area of ​​the cargo compartment. Therefore, multiple subsamples obtained from different locations on the same grain truck together constitute one sample from the grain truck. This means that a sample consists of multiple subsamples, each corresponding to a different sampling location on the same grain truck. The sampling line is used to collect operational information periodically, including at least the number of samples to be processed, the number of arriving samples, the remaining empty bottles on the sampling line, and the number of samples to be transferred at each collection time. Specifically, the number of samples to be processed refers to the number of samples that have entered the sampling process at the current collection time but have not yet been bottled or capped; the number of arriving samples refers to the number of samples that entered the sampling line during the period from the previous collection time to the current collection time; the remaining empty bottles on the sampling line refer to the number of empty sampling bottles available for filling at the current time; and the number of samples to be transferred refers to the number of samples that have been bottled and capped but have not yet been transferred to the storage facility by the bottle handling equipment. The operational status is determined based on the operational information on the sampling line, specifically including:

[0047] Pre-set corresponding reference upper limits include: the upper limit of the acceptable number of samples to be processed under normal operating conditions of the sampling line, the upper limit of the acceptable number of arriving samples within a unit collection cycle, the upper limit of empty bottle remaining capacity, and the upper limit of the number of samples to be transported. Based on these reference upper limits, the number of samples to be processed, the number of arriving samples, the empty bottle remaining capacity of the sampling line, and the number of samples to be transported are standardized to obtain four dimensionless coefficients: the processing pressure coefficient, the arrival impact coefficient, the empty bottle shortage coefficient, and the transport retention coefficient, which are then labeled as follows: , , and Where t is the index of the acquisition time; through (1- The remaining capacity of the unsaturated number of samples to be treated was calculated, (1- The remaining capacity before congestion occurs at the sample input is calculated, and the product of the two values ​​and the inverse is taken to obtain the sample inlet pressure index. This represents the current sampling pressure level; the specific calculation formula is:

[0048]

[0049] It should be noted that when the number of samples to be processed is high, even if the number of newly arriving samples is not large, the sample receiving pressure will still remain at a high level; when the number of samples to be processed is not high but the number of newly arriving samples suddenly increases, the sample receiving pressure will also rise rapidly, thereby avoiding the passivation problem caused by simple linear addition.

[0050] Through (1+ The amplification effect of the current sample receiving pressure on the downstream transfer process is calculated, and then multiplied by the transfer retention coefficient. The transit and retention index was obtained. The range of the transit retention index is limited by min(·, 1), and the specific calculation formula is as follows:

[0051]

[0052] It should be noted that when the sample receiving pressure is high, the samples that have not yet been transferred out of the sample retention line are more likely to accumulate at the rear end, thus the flow retention index will be further amplified; when the sample receiving pressure is low, the amplification effect of the samples to be transferred on the overall congestion is smaller.

[0053] Sample pressure index Transshipment and Circulation Detention Index And empty bottle shortage coefficient By performing hierarchical fusion, the task intensity can be obtained. The specific calculation formula is as follows:

[0054]

[0055] It should be noted that the intensity of the work The value ranges from 0 to 1. The closer the value is to 1, the more intensive the current operation is. The sampling pressure, circulation delay and resource shortage work together to affect the final intensity in the order of front-end sampling pressure, back-end transfer delay and insufficient bottle space resources. The operation intensity will be low only when all three indicators are low. As long as any one of them increases significantly, the overall intensity will be rapidly increased.

[0056] Job density based on each data acquisition time t Connecting the data chronologically, a work intensity curve is plotted. Extreme points are identified within this curve, including maximum and minimum values, i.e., peaks and troughs. All extreme points are sorted according to their data collection time index. Each pair of adjacent extreme points forms an analysis segment. For each segment, the slope of change and the number of threshold exceedances are calculated, and valid segments are marked accordingly. Specifically, this includes:

[0057] The least squares linear fitting method is used to fit the work density corresponding to each collection time in the analysis segment, resulting in a fitted straight line for the analysis segment. The slope of the fitted straight line represents the trend value of the analysis segment, and the slope characterizes the rate of change of work density between two adjacent extreme points. The larger the value, the steeper the rise or fall of the curve in the segment. The number of collection points in the analysis segment with work density greater than a preset density threshold is counted. The preset density threshold is used to distinguish between normal fluctuation state and intensive work state, and in this embodiment, it is set to 0.7. The average slope of the sample line body in the analysis segment during non-intensive work periods is taken and multiplied by an amplification factor to obtain the slope threshold. In this embodiment, the amplification factor is set to 1.5 to make the slope threshold higher than the normal fluctuation level. The number of collection points in the analysis segment is multiplied by a preset percentage and rounded up to obtain the quantity threshold. In this embodiment, the percentage is set to 60%, indicating that at least 60% of the collection points in the analysis segment are above the density threshold before the analysis segment is considered to have obvious intensive characteristics. The analysis segment is recorded as a valid segment when it meets any of the following conditions.

[0058] After obtaining several valid segments, further analysis is performed on these segments to determine whether the current operation is in a sampling-intensive environment. Specifically, this includes: summing the durations of all valid segments to obtain the intensive duration; calculating the total duration of the statistical curve, i.e., the duration of all analyzed segments; dividing the intensive duration by the total duration to obtain the intensive coverage rate; then calculating the local over-threshold area formed by the curve and the intensive threshold within each valid segment. The over-threshold area represents the cumulative degree to which the operation intensity exceeds the threshold; summing the local over-threshold areas of all valid segments to obtain the global over-threshold area; and finally, calculating the intensive over-intensity using the following formula:

[0059]

[0060] Where S represents the global overthreshold area, and T represents the total duration of the curve. The density threshold is represented by R; the density superintensity R represents the cumulative intensity of the work density exceeding the preset threshold throughout the curve.

[0061] When the dense coverage rate is greater than or equal to the first judgment threshold, or the dense over-intensity is greater than or equal to the second judgment threshold, it is determined that the current operation is in a dense sampling task environment; otherwise, it is determined that the current operation is not in a dense sampling task environment. In this embodiment, the first judgment threshold is set to 0.5, which means that when more than half of the analysis window time falls within the effective segment, it is identified as a dense operation environment; the second judgment threshold is set to 1, which means that when the cumulative area of ​​the operation density exceeding the threshold reaches the preset standard, it is identified as a dense operation environment.

[0062] Step 200: When not in a task-intensive state, the sample handling line, bottle handling equipment, transfer equipment, and storage tank are controlled sequentially according to a preset order to complete sample transfer. When a task-intensive state is identified, the connection relationships between the current equipment nodes are obtained, and with the storage tank as the target endpoint, several near-end candidate transfer sequences for the current sample to reach the storage tank are generated. These candidate transfer sequences are then filtered step-by-step to determine the optimal transfer sequence and execute it. Specifically, this includes:

[0063] In this embodiment, node connectivity refers to the traversable connections between the sample retention line, bottle handling equipment, intra-floor transfer equipment, inter-floor transfer equipment, and storage facility; near-end candidate transfer sequences refer to several alternative sequences that can reach the storage facility from the current sample node along existing connections, with fewer transfer steps and shorter transfer distances; the current equipment topology is abstracted as a directed connection graph, where nodes include at least sample retention line nodes, intra-floor transfer nodes, inter-floor transfer nodes, bottle handling equipment nodes, and storage facility nodes, and connecting edges indicate executable transfers between adjacent nodes. The transport channel, starting from the current sample node and ending at the storage node, uses a breadth-first search to generate several candidate transport sequences, provided that node connectivity is satisfied. After obtaining these candidate sequences, each sequence is screened. Specifically, for each node in a candidate transport sequence, the number of tasks currently waiting to be processed and the node's designed processing limit are obtained. The congestion ratio of the node is calculated by dividing the number of tasks currently waiting to be processed by the node's designed processing limit. All nodes in the candidate transport sequence are then selected. The maximum value of the point congestion ratio is used as the bottleneck congestion coefficient of the candidate flow sequence. The bottleneck congestion coefficient is used to characterize the load degree of the most congested node in the candidate flow sequence. To ensure scheduling stability, the bottleneck threshold is preset to 0.8 in this embodiment. When the bottleneck congestion coefficient of a candidate flow sequence is greater than or equal to the bottleneck threshold, the candidate sequence is determined to have a significant congestion risk and is not considered as a preferred sequence. If it is less than the preset bottleneck threshold, it is retained and enters the next layer of comparison. The estimated completion time is calculated for the retained candidate flow sequences. The estimated completion time is determined by the action time of each node in the sequence and the transfer time between each node. The time is composed of several factors. Specifically, the execution time is the sum of the preset action times required for each node in the candidate transfer sequence to perform one action, and the transfer time is the sum of the preset transfer times required for adjacent nodes to complete one transfer. The execution time and transfer time are then summed to obtain the estimated completion time. The above action time and transfer time are pre-calibrated based on historical operating data collected during the equipment debugging phase. The estimated completion time is used to reflect the total time taken from issuing the instruction to the sample completing the transfer in the candidate transfer sequence. The smaller the estimated completion time, the more suitable the sequence is for priority execution under task-intensive conditions.

[0064] The optimal transfer sequence is determined according to the following hierarchical rules: among all near-end candidate transfer sequences, the sequence with the smallest bottleneck occupancy coefficient is selected; if multiple sequences have the same bottleneck occupancy coefficient, their estimated completion times are compared, and the sequence with the smallest estimated completion time is selected; if the estimated completion times are still the same, the sequence with the fewest node switching times is selected; once the optimal transfer sequence is determined, the key nodes and connecting channels in the sequence are temporarily locked to prevent other sample tasks from occupying the same node or channel simultaneously; according to the node order of the optimal transfer sequence, execution instructions are sequentially issued to the sample retention line, bottle handling equipment, transfer equipment, and storage facility to ensure that the sample is transferred and enters the storage facility according to the preset transfer sequence. Once the current node completes its action, it is immediately unlocked and restored to a schedulable state for the next batch of samples to be called. In this way, under the above-mentioned task-intensive conditions, several near-end candidate sequences to the storage are first generated based on the connection relationship between the current nodes. Then, the optimal flow sequence is selected from the candidate sequences according to the bottleneck occupancy level, the expected completion time, and the number of node switching, thereby realizing the rapid and orderly flow of samples and avoiding sample delays or equipment congestion caused by improper path selection.

[0065] Step 300: After the samples are initially stored, the corresponding sample bottles are retrieved at any time according to the sample number for on-demand re-inspection. During re-inspection, the storage facility transfers the target sample bottle to the sampling platform, which automatically opens the bottle for inspection personnel to take samples. After the inspection personnel have taken samples, the sample bottle returns to the sampling platform, which automatically closes the cap and re-acquires the current bottle weight through the weighing module. Based on the weight difference before and after re-inspection and the bottle's condition, the platform automatically determines whether the bottle is currently full, half-full, or empty, and writes this status back to the bottle's chip. Subsequently, the storage of each sample in the storage facility is continuously evaluated, and the retention period of the samples is dynamically determined based on the evaluation results. When the disposal conditions are met, the system automatically performs disposal and empty bottle recycling, specifically including:

[0066] In this embodiment, when the sample bottle enters the sampling platform, the weighing module first records the weight of the bottle before re-inspection. After the inspector takes the sample and recaps it, it records the weight of the bottle after re-inspection. Based on the difference between the two weights and the current bottle status, the module automatically determines whether the bottle is currently full, half-full, or empty. Specifically: when the weight difference is greater than or equal to a preset full-bottle threshold, the sample bottle is determined to be full; when the weight difference is between the preset full-bottle threshold and the preset empty-bottle threshold, the sample bottle is determined to be half-full; when the weight difference is less than or equal to the preset empty-bottle threshold, the sample bottle is determined to be empty. The aforementioned full-bottle threshold and empty-bottle threshold... All thresholds are pre-calibrated by the storage facility upon receipt. Re-inspection operations will change the amount of sample inside the bottle. The original information on the chip alone cannot reflect the actual remaining amount. Therefore, the status of the sample bottle must be reconfirmed through weighing results and written back to the chip on the bottle to ensure the accuracy of the status when it is retrieved again. After the status of the sample bottle is confirmed, the sampling platform writes the status back to the chip on the bottle. If the result is that the bottle is full, the original sample retention status of the sample bottle is maintained. If the result is that the bottle is half full, the sample bottle is marked as partially sampled so that it can be identified first when it is retrieved again. If the result is that the bottle is empty, the sample bottle is marked as empty and awaiting recycling.

[0067] After completing the re-inspection and writing back the status, continuous storage assessments are conducted on each sample in the storage facility. Based on the assessment results, the retention period of the samples is dynamically determined. Specifically, the sample number, subsample number, grain variety, initial storage time, current storage duration, basic retention period, and inspection records for all subsamples are obtained. These inspection records include the number of re-inspections (n) and the number of abnormal records (m). It should be noted that different grain varieties have different requirements for quality stability, dispute verification, and regulatory traceability. Therefore, a corresponding standard retention period should be pre-set based on the grain variety, intended use, inspection items, and sample management requirements to determine the retention period for samples in the absence of re-inspections. The minimum retention period under normal conditions; if the number of re-inspections n and the number of abnormal records m of the sample are both zero, then the basic retention period and the current storage time are directly compared to determine whether to continue storage or discard the sample. Specifically, if the current storage time is less than the basic retention period, the sample is retained; if the current storage time is greater than or equal to the basic retention period, the sample is discarded. If neither the number of re-inspections n nor the number of abnormal records m of the sample is zero, i.e., n≠0 or m≠0, then the analytical retention value index is calculated. Specifically, suppose the sample from the same grain truck contains K sub-samples, and the inspection result of the i-th sub-sample is denoted as... , where i represents the index of the subsample; when there is no retest and no abnormalities, then =0, when a re-inspection is conducted and no abnormalities are found. =0.5; when an abnormality exists and there is no re-inspection, =0.7; when both re-inspection and abnormality exist simultaneously, then =1; The position weight is set according to the sampling location of each subsample, denoted as . It should be noted that the location weights are determined based on their representativeness to the overall grain sample and their sensitivity to local anomalies. Sampling locations in the middle of the length, lateral, and height of the truck bed are generally more representative and therefore assigned higher weights because they are closer to the average loading condition of the entire truck. Sampling locations at the front, rear, left, right, upper, lower, and four corners of the truck bed are more susceptible to factors such as loading and unloading sequence, boundary accumulation, settlement compaction, and localized unbalanced loading, and are more sensitive to local anomalies, thus assigned relatively lower or medium weights. For the central and corner areas of the truck bed, in cases of uneven loading or significant differences in cone shape, the weights are appropriately increased based on their ability to reflect the spread of anomalies, thereby improving the accuracy of identifying the risk distribution of subsamples. The inspection results of each subsample are then... The sample diffusion factor d is calculated using the location weights, and the formula is as follows:

[0068]

[0069] It should be noted that the subsample diffusion factor is used to characterize the degree of diffusion of abnormal or re-examination states at different sampling locations. When multiple high-weight locations correspond to... A larger value indicates a significant risk diffusion of the sample across the entire vehicle's spatial distribution, making it more necessary to retain the entire batch of samples; conversely, if only a few low-weight locations show a larger value... This indicates that the risk is more likely to be localized, and the basis for extending the sample retention period is relatively weak.

[0070] The sample value index V is calculated using the following formula: The sample diffusion factor d, the number of re-inspections n, and the number of abnormal records m.

[0071]

[0072] It should be noted that the inspection records corresponding to each subsample in the sample are used to characterize the overall retention status of the sample at different sampling locations. If any subsample has a re-inspection or anomaly record, it indicates that there may still be unresolved quality disputes or traceability needs for that sample. The larger the subsample diffusion factor, the more the anomaly or re-inspection has spread from a single sampling location to more critical locations. The retention value index is used to characterize the necessity of continuing to retain the sample. The larger the value, the higher the potential value of the sample in subsequent quality traceability, dispute review, and risk verification, and therefore the more necessary it is to continue to retain it. Specifically, value compensation is applied to samples with re-inspection needs or anomaly risks. To prevent the index from being too large and leading to an indefinite extension of the retention period, an upper limit is imposed on the retention value index, resulting in a corrected effective value index. , specifically: According to the revised effective value index Dynamically determine the actual retention period The expression is: ,in The base retention period is defined as λ, which is the extension period correction coefficient used to control the extension range. In this embodiment, the value is 0.5. When the current storage time is less than the actual retention period, the sample continues to be retained. When the current storage time is equal to or greater than the actual retention period, the sample is discarded. After the discard task is generated, the empty bottles are recycled. It should be noted that in the empty bottle recycling process, the empty bottles are cleaned, dried, and cleared of status in sequence. The sample information in the bottle chip is deleted or archived, and only the necessary historical traceability records are retained. The empty bottle is remarked as an empty bottle and returned to the empty bottle area of ​​the storage warehouse. When the remaining empty bottles in the sample retention line are lower than the preset replenishment threshold, they are retrieved from the empty bottle area of ​​the storage warehouse and replenished to the waiting position of the sample retention line so as to enter the next round of automatic sampling and retention process.

[0073] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0074] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. An automated method for storing grain samples in conjunction with an intelligent sampling system, characterized in that, include: Step S100: After the automatic sampling of the grain sample from the incoming vehicle is completed, the automated sampling equipment transports the sample to the receiving position of the sampling line and acquires the basic information of the sample. At the same time, the operation information on the sampling line is collected according to a preset cycle, and the operation density corresponding to each collection time is calculated based on the operation information. The operation density curve is plotted. When the operation density curve meets the preset density judgment condition, it is determined that the current task is in a dense state. Step S200: When the current task is not in a state of high workload, the sample transfer and storage shall be completed in a preset order. When the task is currently intensive, several near-end candidate flow sequences to the storage are generated based on the node connection relationship between the sampling line and the storage. The candidate flow sequences are then screened step by step to determine the optimal flow sequence before execution. In step S300, after the sample is initially stored, the corresponding sample bottle is called according to the sample number to perform on-demand re-inspection, and the status of the sample bottle is written back according to the change in bottle weight before and after the re-inspection. The storage evaluation of each sample in the storage room is carried out, and the retention period of the sample is dynamically determined based on the evaluation results.

2. The automated grain sampling and storage method according to claim 1, characterized in that, The sampling locations include at least two or more of the following: sampling locations along the length of the grain truck compartment at the front, middle, and rear; sampling locations along the lateral direction at the left, middle, and right; and sampling locations along the height direction at the upper, middle, and lower levels. The sampling locations also include sampling locations at the four corners of the compartment and at the center of the compartment.

3. The automated grain sample retention and storage method according to claim 1, characterized in that, The operation information includes at least the number of samples to be processed, the number of samples arriving, the remaining empty bottles in the sample retention line, and the number of samples to be transferred at each collection time. Based on this, the pressure coefficient to be processed, the impact coefficient of arrival, the empty bottle shortage coefficient, and the transfer retention coefficient are obtained respectively. Then, the operation density is calculated by hierarchically fusing the pressure coefficient to be processed, the impact coefficient of arrival, the transfer retention coefficient, and the empty bottle shortage coefficient.

4. The automated grain sample retention and storage method according to claim 3, characterized in that, The determination of the effective segment of the work density curve includes: identifying the extreme points of the work density curve, performing least squares linear fitting on the analysis segment between adjacent extreme points to obtain the changing trend of the analysis segment, i.e., the slope, and counting the number of collection points in the analysis segment whose work density is greater than or equal to a preset density threshold; when the slope of the analysis segment is greater than the slope threshold or the number of collection points reaches the quantity threshold, the analysis segment is recorded as an effective segment.

5. The automated grain sampling and storage method according to claim 4, characterized in that, The operation density curve satisfies the preset density judgment condition as follows: All effective segments are summarized and analyzed; the sum of the durations of all effective segments is used to obtain the dense duration; the total duration corresponding to the operation density curve is calculated; the ratio of the dense duration to the total duration is used as the dense coverage rate; simultaneously, the over-threshold area formed between the operation density curve and the preset density threshold within each effective segment is calculated; the over-threshold areas corresponding to all effective segments are accumulated to obtain the global over-threshold area; and the dense over-intensity is calculated based on the global over-threshold area; when the dense coverage rate is greater than or equal to the first judgment threshold, or the dense over-intensity is greater than or equal to the second judgment threshold, the operation density curve is determined to satisfy the preset density judgment condition.

6. The automated grain sample retention and storage method according to claim 1, characterized in that, In a task-intensive state, the flow process from the sample line to the storage is abstracted as a directed connection graph, and a breadth-first search is used to generate several near-end candidate flow sequences.

7. The automated grain sample retention and storage method according to claim 6, characterized in that, The screening of candidate flow sequences includes: obtaining the number of tasks currently waiting to be processed for each node in the sequence and the designed processing limit of the node, calculating the node congestion ratio, and taking the maximum value of the node congestion ratio as the bottleneck congestion coefficient; when the bottleneck congestion coefficient is greater than or equal to the bottleneck threshold, the corresponding candidate flow sequence is removed; for the retained candidate flow sequences, the sum of the node action time and the transfer time between nodes is further calculated to obtain the expected completion time, and the optimal flow sequence is determined in the order of minimum bottleneck congestion coefficient, minimum expected completion time, and minimum number of node switching.

8. The automated grain sample retention and storage method according to claim 7, characterized in that, After determining the optimal flow sequence, the key nodes and connecting channels in the optimal flow sequence are temporarily locked. The lock is released after the current node completes its action, so as to prevent other sample tasks from occupying the same node or channel at the same time.

9. The automated grain sampling and storage method according to claim 1, characterized in that, During re-inspection, the sampling platform automatically opens the sample bottle for the inspectors to take samples. After the inspectors have taken samples, the sampling platform automatically closes the bottle. The weighing module then obtains the weight difference of the bottle before and after the re-inspection. Based on the weight difference and a preset threshold, the sample bottle is determined to be full, half-full, or empty, and the determination result is written back to the chip on the bottle.

10. The automated grain sampling and storage method according to claim 1 for docking with an intelligent sampling system, characterized in that, The storage assessment includes: obtaining the sample number, subsample number, grain variety, initial storage time, current storage duration, basic retention period, and inspection records for each subsample. The inspection records include the inspection results for each subsample. Based on the inspection results, the number of re-inspections and the number of abnormal records for all subsamples are counted. If both the number of re-inspections (n) and the number of abnormal records (m) for a sample are zero, the basic retention period is directly compared with the current storage duration to determine whether to continue storage or discard the sample. Otherwise, a discrete inspection value is assigned to each subsample based on the inspection results. A position weight is set according to the sampling location of each subsample. The subsample diffusion factor is calculated based on the discrete inspection value and the position weight. The subsample diffusion factor is then combined with the number of re-inspections and the number of abnormal records to calculate the sample retention value index. The actual retention period of the sample is dynamically determined accordingly. Upon expiration, the sample is discarded and the empty bottles are recycled.

Images