A railway bulk grain-based sampling car management system

Abstract

The application discloses a kind of sampling car box management systems based on railway bulk grain, and the application relates to the technical field of sampling car box, solve the problem that original processing mode is not comprehensive enough, the application comprehensively considers the unloading rate curve of the same batch of multiple associated car boxes by sampling car box calibration end, and the sampling car box is reasonably calibrated after rigorous mean value calculation and difference comparison, fully consider the influence of grain impurity factors on unloading rate, ensure that the selected sampling car box is highly representative, greatly improve the accuracy of subsequent quality inspection data, avoid the quality misjudgment caused by false sampling;The associated data analysis end focuses on the unloading rate curve of the sampling car box, accurately locks the maximum difference section and the corresponding characteristic quantity interval, clearly presents the fast and slow abnormal area of unloading in the car box, provides highly targeted sampling guidance for the operator, so that it can quickly locate the key area sampling, improve the sample characteristic, and make the quality inspection result more reliable.

Description

Technical Field

[0001] This invention relates to the field of sampling car technology, specifically a sampling car management system based on bulk grain on railways. Background Technology

[0002] The sampling wagons for bulk grain on railways are a crucial link in the railway bulk grain transportation and quality control system. They bear the important responsibility of obtaining samples that can accurately represent the quality of the entire batch of bulk grain, and play an indispensable role in ensuring fair grain transactions, safe storage, and smooth subsequent processing and utilization.

[0003] The supporting software specially developed for the operation of the automatic sampling equipment on the bulk grain railway line is mainly used to issue sampling instructions and provide real-time feedback and analysis of the machine vision recognition results during the sampling process. In the actual sampling process, relevant operators confirm the sampling car based on their personal operating experience and sample and analyze the goods in the sampling car. However, this method is not comprehensive enough and cannot accurately locate the sampling car or improve the accuracy of its sampling quality inspection. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a sampling car management system for bulk grain on railways, which solves the problem that the original processing methods are not comprehensive enough.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a sampling car management system for bulk grain transport on railways, comprising:

[0006] The visual monitoring terminal monitors the associated cargo containers that are being unloaded in real time and transmits the real-time monitoring images to the data analysis and processing terminal.

[0007] On the data analysis and processing end, based on real-time monitoring of the cargo inside the truck bed, a cargo model of that truck bed is generated. As unloading progresses, the generated cargo model is dynamically updated. Based on the changes in the corresponding cargo model over time, an unloading rate curve for that truck bed is generated. Specifically:

[0008] Based on the generated cargo model, the vertical inflection point of the cargo box outside the cargo model is identified. The associated angle between the points on both sides of the vertical inflection point and this point is 90°. Based on the four sets of vertical inflection points determined above the cargo model, the plane where the four sets of vertical inflection points are located is marked as the reference plane.

[0009] Confirm the initial state of the cargo model, move the reference plane horizontally upwards, and when the reference plane coincides with the highest point of the cargo model in the initial state, mark the reference plane at the current position as the level surface.

[0010] Record the vertical distances between different points within the level plane and the upper surface of the initial cargo model body, and then record the sum of several sets of vertical distances. The confirmed vertical distance sum is calibrated as ZH. Based on the real-time changing cargo model body, the real-time associated vertical distance sum is confirmed in real time. Based on the correlation difference of the vertical distance sum between adjacent time moments, a difference rate curve for this cargo model body is generated. This difference rate curve is calibrated as the unloading rate curve of this car body, where the correlation difference = vertical distance sum at the previous time moment - vertical distance sum at the next time moment;

[0011] At the sampling car body calibration end, the unloading rate curves of multiple related cars body segments in the same batch unloading process are comprehensively confirmed. Based on the different unloading rate curves associated with different related cars body segments, the average unloading speed corresponding to the respective related cars body segments is confirmed. Based on the different average unloading speeds associated with different related cars body segments, the sampling car body segment is selected from several groups of related cars body segments. The specific method is as follows:

[0012] Based on the different unloading rate curves associated with different related wagons in this batch, the different unloading rates corresponding to different times are identified from the corresponding unloading rate curves. The average unloading rate of several sets of unloading rates is averaged to lock the average unloading speed of this related wagon, and the average unloading speed of other related wagons is confirmed in turn.

[0013] The average unloading speeds associated with different related wagons in this batch are calibrated as J. i Where i represents different associated car bodies, and the unloading average speed J of several groups is... i Then perform mean averaging to lock the feature value Tz;

[0014] This will satisfy: |J i The associated car body with -Tz|≥Y1 is calibrated as the sampling car body, where Y1 is a preset value; otherwise, no calibration is performed.

[0015] The associated data analysis terminal, based on the calibrated sampling truck, first confirms the unloading rate curve associated with this sampling truck, and confirms its average unloading speed mark based on this unloading rate curve. Starting from the initial point of the unloading rate curve, the curve segments are confirmed in sequence, and based on the specific differences between the curve segments and the average unloading speed mark, the segment with the largest difference is locked, and the feature quantity interval is locked based on the segment with the largest difference. The locked feature quantity interval is then displayed through the display terminal.

[0016] The preferred method for locking the segment with the greatest difference is as follows:

[0017] Based on the unloading rate curve associated with the corresponding sampling car body, and the average unloading speed J associated with this unloading rate curve. i Determine the average unloading speed J in the coordinate system containing the unloading rate curve. iThe associated standard horizontal line is parallel to the horizontal axis of this coordinate system.

[0018] Define a set of line segment length intervals as [X1, X2], where X1 and X2 are preset values, representing the minimum and maximum lengths of the corresponding selected line segments;

[0019] The length of the selected line segment is limited to X1. The first selection process is executed: starting from the initial point of the unloading rate curve, the line segment is selected step by step. The line segment points inside the selected line segment are confirmed. The vertical distances of several line segment points to the standard horizontal line are determined. Several sets of vertical distances are summed to determine the line segment characteristics of this selected line segment.

[0020] After the line segments associated with the initial point are selected, the selected line segments associated with subsequent points are confirmed in turn, and the line segment characteristics of the corresponding selected line segments are confirmed simultaneously. This process continues until the end point of the selected line segment corresponds to the end point of the unloading rate curve.

[0021] The length of the selected line segment is gradually increased in the range [X1, X2], and different selection processes are executed. The selection method of each different selection process is the same as that of the first selection process, but the length of the selected line segment associated with each different selection process is different.

[0022] From several different selection processes, identify the different segment features associated with several different selected line segments. From several sets of line segment features, select the line segment feature with the maximum value as the standard feature, and take the selected line segment associated with this standard feature as the maximum difference segment.

[0023] Preferably, the specific method for locking the feature quantity interval from the segment with the greatest difference is as follows:

[0024] Based on the determined maximum difference segment, the first and last times associated with the beginning and end of the maximum difference segment are locked. The unloading rates associated with several sets of times before the first time are summed to confirm the total rate V1 of the first time. The first feature associated with the first time is confirmed by using V1 × unit time = first feature, where the unit time is a preset time. The unloading rates associated with several sets of times before the last time are summed to confirm the total rate V2 of the last time. The last feature associated with the last time is confirmed by using V2 × unit time = last feature.

[0025] Based on the first and last features identified by the segment with the greatest difference, a set of feature intervals is generated, and the identified feature intervals are displayed accordingly.

[0026] This invention provides a sampling car management system for bulk grain transport on railways. Compared with existing technologies, it has the following advantages:

[0027] This invention comprehensively considers the unloading rate curves of multiple related wagons in the same batch through the sampling wagon calibration end. Through rigorous mean calculation and difference comparison, the sampling wagon is reasonably calibrated, fully taking into account the impact of grain impurities on the unloading rate, ensuring that the selected sampling wagon is highly representative, greatly improving the accuracy of subsequent quality inspection data, and avoiding quality misjudgment due to incorrect sampling.

[0028] The associated data analysis terminal focuses on the unloading rate curve of the sampling container, accurately identifies the segment with the greatest difference and the corresponding characteristic range, and clearly presents the abnormal areas of unloading speed in the container. This provides operators with highly targeted sampling guidance, enabling them to quickly locate key areas for sampling, improve sample characteristics, and make quality inspection results more reliable. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the principle framework of the present invention;

[0030] Figure 2 This is a schematic diagram illustrating the determination of line segments in this invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Please see Figure 1 This application provides a sampling car management system based on bulk grain on railways, including a visual monitoring terminal, a data analysis and processing terminal, a sampling car calibration terminal, a correlation data analysis terminal, and a display terminal. The visual monitoring terminal is electrically connected to the input node of the data analysis and processing terminal, and the data analysis and processing terminal is electrically connected to the input node of the sampling car calibration terminal or the correlation data analysis terminal, respectively. The sampling car calibration terminal, the correlation data analysis terminal, and the display terminal are all electrically connected from the output node to the input node.

[0033] Among them, the visual monitoring terminal monitors the associated trucks that are being unloaded in real time and transmits the real-time monitoring images to the data analysis and processing terminal. The monitored images include images from multiple angles, including images of the cargo inside the vehicle from different angles. The cargo includes different types of grains, such as wheat and soybeans.

[0034] In the data analysis and processing section, based on real-time monitoring of the cargo inside the truck bed, a cargo model for that truck bed is generated. As unloading progresses, the generated cargo model undergoes real-time correlation changes. Since generating corresponding models based on multi-terminal monitoring images is a common technique in existing technology, it will not be elaborated upon here. All monitored images are monitored by high-precision probes, allowing real-time confirmation of changes in the grain content of the loaded cargo, thereby enabling real-time correction of the specified cargo model. Based on the changes in the corresponding cargo model over time, an unloading rate curve for that truck bed is generated. The specific method for generating this unloading rate curve is as follows:

[0035] Based on the generated cargo model, the vertical inflection point of the cargo box outside the cargo model is identified. The associated angle between the points on both sides of the vertical inflection point and this point is 90°. Based on the four sets of vertical inflection points determined above the cargo model, the plane where the four sets of vertical inflection points are located is marked as the reference plane.

[0036] Confirm the initial state of the cargo model, move the reference plane horizontally upwards, and when the reference plane coincides with the highest point of the cargo model in the initial state, mark the reference plane at the current position as the level surface.

[0037] Record the vertical distances between different points on the level plane and the upper surface of the initial cargo model. Record the sum of several sets of vertical distances. Label the confirmed vertical distance sum as ZH. Based on the real-time changes of the cargo model, confirm the real-time associated vertical distance sum. Based on the correlation difference between the vertical distance sums of adjacent times, generate a difference rate curve for this cargo model. Label this difference rate curve as the unloading rate curve of this cargo box. The correlation difference = vertical distance sum of the previous time - vertical distance sum of the next time. Specifically, according to the real-time unloading process, the grain stored in the corresponding cargo box will gradually decrease. During the decrease, the corresponding model will undergo correlation changes. Thus, the corresponding rate change curve can be determined based on the corresponding real-time change process. The real-time change process will drive the associated distance parameters to change. Thus, the distance change of each point can lock the overall change state of the corresponding cargo in the cargo box, thereby locking the unloading rate change.

[0038] The sampling carbox calibration end comprehensively confirms the unloading rate curves of multiple related cars in the same batch unloading process (seven or eight cars will unload simultaneously during each batch). Based on the different unloading rate curves associated with different related cars, the average unloading speed of the corresponding related cars is confirmed. Based on the different average unloading speeds associated with different related cars, a sampling carbox is selected from several groups of related cars. The grain to be unloaded in the same batch is all of the same type. If they are different types of grain, it will lead to a large error in data verification and make it impossible to accurately determine the sampling carbox. The specific method for selecting the sampling carbox is as follows:

[0039] Based on the different unloading rate curves associated with different related wagons in this batch, the different unloading rates corresponding to different times are identified from the corresponding unloading rate curves. The average unloading rate of several sets of unloading rates is averaged to lock the average unloading speed of this related wagon, and the average unloading speed of other related wagons is confirmed in turn.

[0040] The average unloading speeds associated with different related wagons in this batch are calibrated as J. i Where i represents different associated car bodies, and the unloading average speed J of several groups is... i Then perform mean averaging to lock the feature value Tz;

[0041] This will satisfy: |J i The associated car body with -Tz|≥Y1 is calibrated as the sampling car body; otherwise, no calibration is performed. Y1 is a preset value, and its specific value is determined by the operator based on experience.

[0042] Specifically, during the unloading process of the sampling car body, the unloading rate varies due to the different impurities contained in the grain inside. Many car bodies contain large impurity particles, while some car bodies do not contain any impurities. This results in different unloading rates for the same type of grain in the corresponding car bodies. This difference in impurities is the reason for the difference in unloading rates. The corresponding sampling car body is identified from the corresponding average speed to ensure the accuracy of the sampling car body identification.

[0043] In the data analysis section, based on the calibrated sampling truck, the unloading rate curve associated with the sampling truck is first identified, and the average unloading speed mark is identified based on this unloading rate curve. Starting from the initial point of the unloading rate curve, the curve segments are identified sequentially. Based on the specific differences between the curve segments and the average unloading speed mark, the segment with the largest difference is locked, and the characteristic quantity range is locked based on the segment with the largest difference. The locked characteristic quantity range is displayed through the display end. Specifically, during the unloading process of the corresponding sampling truck, there will be different areas with different unloading speeds. The relevant areas with fast unloading are relatively normal, and the relevant areas with slow unloading are relatively abnormal. Based on the corresponding unloading rate curve, the corresponding more abnormal curve segments are locked from the corresponding curve. Based on these curve segments, the corresponding unloading volume is determined, so as to facilitate relevant operators to take better samples, achieve better sampling results, and ensure that the sampling is more characteristic.

[0044] The specific method for locking the segment with the largest difference is as follows:

[0045] Based on the unloading rate curve associated with the corresponding sampling car body, and the average unloading speed J associated with this unloading rate curve. i Determine the average unloading speed J in the coordinate system containing the unloading rate curve. i The associated standard horizontal line is parallel to the horizontal axis of this coordinate system, which is the time line and its vertical axis is the rate line.

[0046] S1. Determine a set of line segment length intervals as [X1, X2], where X1 and X2 are preset values, determined by relevant operators based on experience, representing the minimum and maximum lengths of the corresponding selected line segments;

[0047] S2. Limit the length of the selected line segment to X1 and execute the first set of selection processes: starting from the initial point of the unloading rate curve, select line segments step by step, confirm the line segment points inside the selected line segment, determine the vertical distance between several line segment points and the standard horizontal line, and sum the several sets of vertical distances to determine the line segment characteristics of this selected line segment.

[0048] After the line segments associated with the initial point are selected, the selected line segments associated with subsequent points are confirmed in turn, and the line segment characteristics of the corresponding selected line segments are confirmed simultaneously. This process continues until the end point of the selected line segment corresponds to the end point of the unloading rate curve.

[0049] S3. Gradually increase the length of the selected line segment in [X1, X2] and execute different selection processes. The selection method of each different selection process is the same as that of the first group of selection processes, but the length of the selected line segment associated with each different selection process is different.

[0050] S4. From several different selection processes, identify the different segment features associated with several different selected line segments. From several line segment features, select the line segment feature with the maximum value as the standard feature, and take the selected line segment associated with this standard feature as the maximum difference segment.

[0051] Specifically, such as Figure 2 As shown, after confirming the unloading rate curve associated with the corresponding sampling truck, the corresponding mean value is determined based on the corresponding rate parameter, thereby locking the corresponding standard horizontal line. Furthermore, within the corresponding unloading rate curve, line segments can be selected from front to back. Figure 2 As can be seen from the data, there exists a set of line segments with a length of X1. Different points within this selected line segment are relatively close to the standard horizontal line, so the corresponding values ​​can be locked from it. This selected line segment does not deviate too much from the corresponding standard horizontal line, so it is generally not selected. In the process of determining from front to back, this method can be used to quickly determine the line segments with large value differences, thereby determining the corresponding segment with the largest difference.

[0052] The specific method for locking down the feature interval from the identified largest difference segment is as follows:

[0053] Based on the determined maximum difference segment, the first and last times associated with the beginning and end of the maximum difference segment are locked. The unloading rates associated with several sets of times before the first time are summed to confirm the total rate V1 of the first time. The first characteristic associated with the first time is confirmed by using V1 × unit time = first characteristic, where the unit time is a preset time, which is determined in advance by relevant operators and is generally confirmed based on the corresponding rate value associated with the unit. The unloading rates associated with several sets of times before the last time are summed to confirm the total rate V2 of the last time. The last characteristic associated with the last time is confirmed by using V2 × unit time = last characteristic.

[0054] Based on the first and last features identified by the segment with the greatest difference, a set of feature ranges is generated and displayed on the display device.

[0055] External personnel, based on the displayed characteristic range, perform measurement confirmation from the corresponding storage warehouse, lock in the associated measurement range, and select samples from the corresponding measurement range for testing to assess the quality control compliance of the samples.

[0056] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0057] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A sampling car management system for bulk grain transport on railways, characterized in that, include: The visual monitoring terminal monitors the associated cargo containers that are being unloaded in real time and transmits the real-time monitoring images to the data analysis and processing terminal. On the data analysis and processing end, based on the real-time monitoring of the cargo inside the truck bed, a cargo model of the truck bed is generated. As the unloading process proceeds, the generated cargo model is controlled in real time to make related changes. Based on the change process of the corresponding cargo model over time, an unloading rate curve of the truck bed is generated. The sampling car body calibration end comprehensively confirms the unloading rate curves of multiple related cars body in the same batch unloading process. Based on the different unloading rate curves associated with different related cars body, the average unloading speed of the corresponding related cars body is confirmed. Based on the different average unloading speeds associated with different related cars body, the sampling car body is selected from several groups of related cars body. The associated data analysis terminal, based on the calibrated sampling truck, first confirms the unloading rate curve associated with this sampling truck, and confirms its average unloading speed mark based on this unloading rate curve. Starting from the initial point of the unloading rate curve, the curve segments are confirmed in sequence, and based on the specific differences between the curve segments and the average unloading speed mark, the segment with the largest difference is locked, and the feature quantity interval is locked based on the segment with the largest difference. The locked feature quantity interval is then displayed through the display terminal. The specific method for selecting the sampling vehicle at the sampling vehicle calibration end is as follows: Based on the different unloading rate curves associated with different related wagons in this batch, the different unloading rates corresponding to different times are identified from the corresponding unloading rate curves. The average unloading rate of several sets of unloading rates is averaged to lock the average unloading speed of this related wagon, and the average unloading speed of other related wagons is confirmed in turn. The average unloading speeds associated with different related wagons in this batch are calibrated as J. i Where i represents different associated car bodies, and the unloading average speed J of several groups is... i Then perform mean averaging to lock the feature value Tz; This will satisfy: |J i The associated car body with -Tz|≥Y1 is designated as the sampling car body, where Y1 is a preset value; The specific method for identifying the segment with the greatest difference in the related data analysis terminal is as follows: Based on the unloading rate curve associated with the corresponding sampling car body, and the average unloading speed J associated with this unloading rate curve. i Determine the average unloading speed J in the coordinate system containing the unloading rate curve. i The associated standard horizontal line is parallel to the horizontal axis of this coordinate system. Define a set of line segment length intervals as [X1, X2], where X1 and X2 are preset values, representing the minimum and maximum lengths of the corresponding selected line segments; The length of the selected line segment is limited to X1. The first selection process is executed: starting from the initial point of the unloading rate curve, the line segment is selected step by step. The line segment points inside the selected line segment are confirmed. The vertical distances of several line segment points to the standard horizontal line are determined. Several sets of vertical distances are summed to determine the line segment characteristics of this selected line segment. After the line segments associated with the initial point are selected, the selected line segments associated with subsequent points are confirmed in turn, and the line segment characteristics of the corresponding selected line segments are confirmed simultaneously. This process continues until the end point of the selected line segment corresponds to the end point of the unloading rate curve. The length of the selected line segment is gradually increased in the range [X1, X2], and different selection processes are executed. The selection method of each different selection process is the same as that of the first selection process, but the length of the selected line segment associated with each different selection process is different. From several different selection processes, identify the different segment features associated with several different selected line segments. From several sets of line segment features, select the line segment feature with the maximum value as the standard feature, and take the selected line segment associated with this standard feature as the maximum difference segment.

2. The sampling car management system for bulk grain on railways according to claim 1, characterized in that, The data analysis and processing terminal generates the unloading rate curve of this truck body in the following specific way: Based on the generated cargo model, the vertical inflection point of the cargo box outside the cargo model is identified. The associated angle between the points on both sides of the vertical inflection point and this point is 90°. Based on the four sets of vertical inflection points determined above the cargo model, the plane where the four sets of vertical inflection points are located is marked as the reference plane. Confirm the initial state of the cargo model, move the reference plane horizontally upwards, and when the reference plane coincides with the highest point of the cargo model in the initial state, mark the reference plane at the current position as the level surface. Record the vertical distances between different points on the level plane and the upper surface of the initial cargo model. Then, record the sum of several sets of vertical distances. The confirmed sum of vertical distances is calibrated as ZH. Based on the real-time changing cargo model, the real-time associated sum of vertical distances is confirmed. Based on the correlation difference of the sum of vertical distances between adjacent time moments, a difference rate curve for this cargo model is generated. This difference rate curve is calibrated as the unloading rate curve of this cargo box, where the correlation difference = the sum of vertical distances at the previous time moment - the sum of vertical distances at the next time moment.

3. The sampling car management system for bulk grain on railways according to claim 2, characterized in that, For those that do not meet the following conditions: |J i The associated car body with -Tz|≥Y1 is not calibrated.

4. A sampling car management system for bulk grain on railways according to claim 3, characterized in that, The specific method by which the associated data analysis terminal locks the feature quantity interval from the segment with the greatest difference is as follows: Based on the determined maximum difference segment, the first and last times associated with the beginning and end of the maximum difference segment are locked. The unloading rates associated with several sets of times before the first time are summed to confirm the total rate V1 of the first time. The first characteristic associated with the first time is confirmed by using V1 × unit time = first characteristic, where the unit time is a preset time. The unloading rates associated with several sets of times before the last time are summed to confirm the total rate V2 of the last time. The last characteristic associated with the last time is confirmed by using V2 × unit time = last characteristic. Based on the first and last features identified by the segment with the greatest difference, a set of feature quantity intervals is generated.

5. A sampling car management system for bulk grain on railways according to claim 4, characterized in that, The display terminal will display the determined feature range.

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