Sampling carriage management system based on railway bulk grain

Abstract

The invention discloses a sampling carriage management system based on railway bulk grain, relates to the technical field of sampling carriages, solves the problem that an original processing mode is not comprehensive enough, comprehensively considers unloading rate curves of multiple sections of associated carriages in the same batch through a sampling carriage calibration end, and obtains the unloading rate curve of the multiple sections of associated carriages in the same batch through rigorous mean value calculation and difference value comparison. Sampling carriages are reasonably calibrated, the influence of grain impurity factors on the unloading rate is fully considered, it is guaranteed that the selected sampling carriages are extremely representative, the accuracy of subsequent quality inspection data is greatly improved, and quality misjudgment caused by wrong sampling is avoided; and the associated data analysis end focuses and samples a carriage unloading rate curve, accurately locks the maximum difference section and the corresponding characteristic quantity interval, clearly presents an unloading speed abnormal area in the carriage, provides targeted sampling guidance for an operator, can rapidly position a key area for sampling, improves the characteristic of the sample, and makes a quality inspection result more reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of sampling carriages, and particularly to a sampling carriage management system based on railway bulk grain. Background Art

[0002] The sampling carriage of railway bulk grain is a crucial link in the railway bulk grain transportation and quality control system. It shoulders the heavy responsibility of obtaining samples that can accurately represent the quality of the entire batch of bulk grain, and plays an indispensable role in ensuring the fairness of grain transactions, the safety of warehousing, and the smooth progress of subsequent processing and utilization.

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

[0004] Aiming at the deficiencies of the prior art, the present invention provides a sampling carriage management system based on railway bulk grain, which solves the problem that the original processing method is not comprehensive enough.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A sampling carriage management system based on railway bulk grain, comprising: A visual monitoring terminal that monitors the associated carriage being unloaded in real time and transmits the real-time monitored images to the data analysis and processing terminal; A data analysis and processing terminal that generates a cargo model body of this carriage based on the real-time monitored images of the cargo inside the carriage. As the unloading process progresses, it controls the generated cargo model body to change associatively in real time. Based on the change process of the corresponding cargo model body over time, it generates a unloading rate curve of this carriage. The specific method is as follows: Based on the generated cargo model body, confirm the vertical inflection points of the outer compartment of this cargo model body. The associated angles formed between the points on both sides of the vertical inflection points and this point are 90°. Based on the four groups of vertical inflection points determined above the cargo model body, calibrate the plane where the four groups of vertical inflection points are located as the reference plane; Confirm the initial state of the cargo model body, and move the reference plane horizontally upward. When the reference plane coincides with the highest point of the cargo model body in the initial state, calibrate the current position of the reference plane as the level surface; Record the vertical distances between different points within the reference level surface and the upper surface of the cargo model body in the initial state, then record the sum values of several groups of vertical distances. Calibrate the confirmed vertical distance sum as ZH. Then, based on the real-time changing cargo model body, confirm the real-time associated vertical distance sum in real time, and generate a difference rate curve for this cargo model body based on the relevant differences between the vertical distance sums at adjacent moments. Calibrate this difference rate curve as the unloading rate curve of this carriage, where the relevant difference = the vertical distance sum at the previous moment - the vertical distance sum at the next moment; At the calibrated end of the sampling carriage, comprehensively confirm the unloading rate curves of multiple associated carriages in the same batch of unloading processes. Based on the different unloading rate curves associated with different associated carriages, confirm the corresponding average unloading speeds of the corresponding associated carriages. Based on the different average unloading speeds associated with different associated carriages, select the sampling carriage from several groups of associated carriages. The specific method is as follows: Based on the different unloading rate curves associated with different associated carriages in this batch, confirm the different unloading rates corresponding to different moments from the corresponding unloading rate curves. Perform mean processing on several groups of unloading rates to lock the average unloading speed of this associated carriage, and sequentially confirm the average unloading speeds of other associated carriages; Calibrate the different average unloading speeds associated with different associated carriages in this batch as J i , where i represents different associated carriages. Perform mean processing on several groups of average unloading speeds J i and lock the characteristic value Tz; Calibrate the associated carriage that satisfies: |J i - Tz|≥Y1 as the sampling carriage, where Y1 is a preset value. Otherwise, no calibration is performed; The associated data analysis terminal, based on the calibrated sampling carriage, preferentially confirms the unloading rate curve associated with this sampling carriage, and based on this unloading rate curve, confirms its average unloading speed marking line. Starting from the initial point of the unloading rate curve, sequentially confirm the curve segments backward, and based on the specific differences between the curve segments and the average unloading speed marking line, lock the maximum difference segment, and based on the maximum difference segment, lock the characteristic quantity interval, and display the locked characteristic quantity interval through the display terminal; Preferably, the specific method for locking the maximum difference segment is as follows: Based on the unloading rate curve associated with the corresponding sampling carriage and the average unloading speed J i associated with this unloading rate curve, determine the standard horizontal line associated with the average unloading speed J i in the coordinate system where the unloading rate curve is located. This standard horizontal line is parallel to the horizontal coordinate axis of this coordinate system; Determine a set of line segment length intervals as [X1, X2], where both X1 and X2 are preset values, representing the minimum length and the maximum length of the corresponding selected line segments; 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, gradually select line segments backward, confirm the line segment points inside the selected line segment, determine the vertical distances between several line segment points and the standard horizontal line, and sum up several groups of vertical distances to determine the line segment characteristics of this selected line segment; After the line segment selection associated with the initial point is completed, sequentially confirm the selected line segments associated with subsequent points, and simultaneously confirm the line segment characteristics of the corresponding selected line segments, and so on, until the end point of the selected line segment corresponds to the end point of the unloading rate curve; Gradually increase the length of the selected line segment within [X1, X2], and execute subsequent different selection processes. The selection method for each different selection process is the same as that of the first set of selection processes, but the lengths of the selected line segments associated with different selection processes are all different; From several groups of different selection processes, confirm the different line segment characteristics associated with several groups of different selected line segments. From several groups of line segment characteristics, select the line segment characteristic in the maximum state value as the standard characteristic, and use the selected line segment associated with this standard characteristic as the maximum difference segment.

[0006] Preferably, the specific method for locking the characteristic quantity interval from the maximum difference segment is as follows: Based on the determined maximum difference segment, lock the start time and end time associated with the start end and end end of the maximum difference segment. Sum up several groups of unloading rates associated with several groups of times in front of the start time to confirm the total rate V1 at the start time. Use V1×unit time = start characteristic to confirm the start characteristic associated with the start time, where the unit time is the preset time. Sum up several groups of unloading rates associated with several groups of times in front of the end time to confirm the total rate V2 at the end time. Use V2×unit time = end characteristic to confirm the end characteristic associated with the end time; Generate a group of characteristic quantity intervals based on the start characteristic and end characteristic confirmed by the maximum difference segment, and perform relevant display on the determined characteristic quantity intervals.

[0007] The present invention provides a sampling carriage management system based on railway bulk grain. Compared with the prior art, it has the following beneficial effects: The present invention comprehensively considers the unloading rate curves of multiple connected carriages in the same batch through the calibration end of the sampling carriage. After rigorous mean calculation and difference comparison, the sampling carriage is reasonably calibrated, fully considering the influence of grain impurity factors on the unloading rate, ensuring that the selected sampling carriage is highly representative, greatly improving the accuracy of subsequent quality inspection data, and avoiding quality misjudgment caused by incorrect sampling; The associated data analysis terminal focuses on the unloading rate curve of the sampling carriage, accurately locks the maximum difference segment and the corresponding characteristic quantity interval, clearly presents the abnormal area of fast and slow unloading in the carriage, provides highly targeted sampling guidance for operators, enables them to quickly locate the key area for sampling, improves the sample characteristics, and makes the quality inspection results more reliable. Description of the Drawings

[0008] Figure 1 It is a schematic diagram of the principle framework of the present invention; Figure 2 It is a schematic diagram for determining the selected line segment of the present invention. Detailed Embodiment

[0009] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0010] Please refer to Figure 1 , this application provides a sampling carriage management system based on railway bulk grain, including a visual monitoring terminal, a data analysis and processing terminal, a sampling carriage calibration terminal, an associated data analysis terminal, and a display terminal. Among them, 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 carriage calibration terminal or the associated data analysis terminal respectively. Moreover, the sampling carriage calibration terminal, the associated data analysis terminal, and the display terminal are all electrically connected from the output node to the input node; Among them, the visual monitoring terminal monitors the associated carriage that is unloading in real time and transmits the real-time monitored picture to the data analysis and processing terminal. The monitored picture includes pictures from multiple angles, and the monitored picture includes pictures of goods at different angles inside the vehicle. The goods are different types of grains, such as wheat, soybeans, etc.; Among them, the data analysis and processing terminal generates a cargo model body of this carriage based on the real-time monitored picture of the cargo inside the carriage. As the unloading process progresses, it controls the generated cargo model body to change associatively in real time. Since the method of generating the corresponding model body based on the monitored multi-terminal pictures is relatively common in the prior art, it will not be elaborated here too much. And the monitored pictures are all monitored by high-precision probes, which can confirm the change of the grains of the corresponding loaded goods in real time, so as to correct the specified cargo model in real time. Based on the change process of the corresponding cargo model body over time, an unloading rate curve of this carriage is generated. The specific method of generating the unloading rate curve of this carriage is as follows: Based on the generated cargo model body, confirm the vertical inflection points of the outer cargo compartment of this cargo model body. The associated angle generated between the points on both sides around the vertical inflection point and this point is 90°. Based on the four groups of vertical inflection points determined above the cargo model body, calibrate the plane where the four groups of vertical inflection points are located as the reference plane; Confirm the initial state of the cargo model body, and horizontally move the reference plane upward. When the reference plane coincides with the highest point of the cargo model body in the initial state, calibrate the reference plane at the current position as the level surface; Record the vertical distances between different points in the level surface and the upper surface of the cargo model body in the initial state, and then record the sum values of several groups of vertical distances. Calibrate the sum of the confirmed vertical distances as ZH. Then, based on the real-time changing cargo model body, confirm the sum of the vertically associated distances in real time, and generate a difference rate curve for this cargo model body based on the relevant differences in the sum of vertical distances between adjacent moments. Calibrate this difference rate curve as the unloading rate curve of this carriage. Among them, the relevant difference = the sum of vertical distances at the previous moment - the sum of vertical distances at the next moment. Specifically, according to the real-time unloading process, the grain stored in the corresponding cargo compartment will gradually decrease. During the decreasing process, the corresponding model body will undergo associated changes. Thus, based on the corresponding real-time change process, the corresponding rate change curve can be determined. Its real-time change process will drive the associated distance parameters to change. Thus, the distance change at each point can lock the overall change state of the corresponding cargo in the cargo compartment to lock the change situation of the unloading rate; Among them, at the calibration end of the sampling carriage, comprehensively confirm the unloading rate curves of multiple associated carriages in the same batch of unloading processes (when unloading each batch, seven or eight carriages will unload synchronously). Based on the different unloading rate curves associated with different associated carriages, confirm the corresponding unloading average speed of the corresponding associated carriage. Based on the different unloading average speeds associated with different associated carriages, select a sampling carriage from several groups of associated carriages. The grains to be unloaded in the same batch are all of the same type of grain. If they belong to different types of grain, it will cause large errors in data verification and cannot accurately determine the sampling carriage. The specific method for selecting the sampling carriage is as follows: Based on the different unloading rate curves associated with different associated carriages in this batch, confirm the different unloading rates corresponding to different moments from the corresponding unloading rate curves, perform mean processing on several groups of unloading rates to lock the unloading average speed of this associated carriage, and sequentially confirm the unloading average speeds of other associated carriages; Calibrate the different unloading average speeds associated with different associated carriages in this batch as J i , where i represents different associated carriages, and several groups of unloading average speeds J i Then perform mean processing again to lock the characteristic value Tz; Satisfy: |J i-The associated carriage with Tz|≥Y1 is calibrated as the sampling carriage, otherwise, no calibration is performed, where Y1 is a preset value, and its specific value is determined by the operator based on experience; Specifically, during the unloading process of the sampling carriage, the unloading rates are different due to the different impurities contained in the grain inside. There are large impurity particles in the grain in many cargo compartments, and there are no impurities in the grain in some cargo compartments. This will cause the unloading rates of the same type of grain in the corresponding carriages to be different when unloading. This is the difference in impurities. The corresponding sampling carriage is locked from the corresponding average speed to ensure the specific accuracy of the sampling carriage confirmation.

[0011] Among them, the associated data analysis end, based on the calibrated sampling carriage, first confirms the unloading rate curve associated with the sampling carriage, and confirms its unloading average speed line based on the unloading rate curve, starting from the initial point of the unloading rate curve, confirms the curve segmentation in sequence, and based on the specific difference between the curve segmentation and the unloading average speed line, locks the maximum difference segment, and locks the feature quantity interval based on the maximum difference segment, and displays the locked feature quantity interval through the display end. Specifically, during the unloading process of the corresponding sampling carriage, 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. According to the corresponding unloading rate curve, the corresponding relatively abnormal curve segments are locked from the corresponding curve, and the corresponding unloading volume is determined based on such curve segments, so as to facilitate the relevant operators to perform better sampling, so as to achieve better sampling effects and ensure that the sampling is more characteristic. Among them, the specific method of locking the maximum difference segment is: Based on the unloading rate curve associated with the corresponding sampling car box and the unloading average speed J associated with this unloading rate curve i , determine the average unloading speed J in the coordinate system of the unloading speed curve i The associated standard horizontal line is parallel to the horizontal axis of the coordinate system. The horizontal axis of the coordinate system is the time line, and the vertical axis is the rate line; S1, determining a set of line segment length intervals as [X1, X2], where X1 and X2 are preset values, which are formulated by relevant operators based on experience, and represent the minimum length and maximum length of the corresponding selected line segments; S2. Limit the length of the selected line segment to X1, and execute the first group of selection processes: starting from the initial point of the unloading rate curve, gradually select the line segment, confirm the line segment points inside the selected line segment, determine the vertical distances between several line segment points and the standard horizontal line, and sum up several groups of vertical distances to determine the line segment characteristics of the selected line segment; After the line segments associated with the initial points are selected, then sequentially confirm the selected line segments associated with subsequent points, and simultaneously confirm the line segment features of the corresponding selected line segments, and so on, until the end point of the selected line segment corresponds to the end point of the unloading rate curve; S3. Gradually increase the length of the selected line segment within [X1, X2], and execute subsequent different selection processes. The selection methods of each different selection process are the same as those of the first group of selection processes, but the lengths of the selected line segments associated with different selection processes are all different; S4. From several groups of different selection processes, confirm the different line segment features associated with several groups of different selected line segments. From several groups of line segment features, select the line segment feature in the maximum state value as the standard feature, and use the selected line segment associated with this standard feature as the maximum difference segment; Specifically, as Figure 2 shown, after the unloading rate curve associated with the corresponding sampling carriage is confirmed, based on the corresponding rate parameters, determine the corresponding mean value, thereby locking the corresponding standard horizontal line. And in the corresponding unloading rate curve, the selected line segments can be determined from front to back. As Figure 2 can be seen from, there is a group of selected line segments with a length of X1. The different points within this selected line segment are relatively close to the standard horizontal line. Therefore, the corresponding relevant values can be locked from it. This selected line segment does not deviate too much from the corresponding standard horizontal line and is generally not selected. In the determination process from front to back, this method can be used to quickly determine the part of the line segment with a large difference in values, so as to determine the corresponding maximum difference segment.

[0012] The specific method for locking the characteristic quantity interval from the confirmed maximum difference segment is: Based on the determined maximum difference segment, lock the start time and end time associated with the start end and end end of the maximum difference segment. Sum up the several groups of unloading rates associated with several groups of times in front of the start time to confirm the total rate V1 at the start time. Use V1 × unit time = start feature to confirm the start feature associated with the start time, where the unit time is a preset time, which is determined in advance by relevant operators, generally confirmed according to the unit associated with the corresponding rate value. Sum up the several groups of unloading rates associated with several groups of times in front of the end time to confirm the total rate V2 at the end time. Use V2 × unit time = end feature to confirm the end feature associated with the end time; Based on the start feature and end feature confirmed by the maximum difference segment, generate a group of characteristic quantity intervals and display them through the display end; External relevant personnel, based on the displayed characteristic quantity intervals, conduct measurement confirmation from the corresponding storage warehouse, lock the associated measurement interval, and select sampling samples from the corresponding measurement interval for inspection to evaluate the quality inspection compliance of the sampling samples.

[0013] Some of the data in the above formula are numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0014] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A sampling carriage management system based on railway bulk grain, characterized in that: include: The visual monitoring end monitors the associated carriages being unloaded in real time and transmits the real-time monitoring images to the data analysis and processing end; The data analysis and processing end generates a cargo model of the carriage based on the cargo image inside the carriage monitored in real time. As the unloading process progresses, the generated cargo model is controlled in real time to change in association. Based on the change process of the corresponding cargo model over time, an unloading rate curve of the carriage is generated. The sampling carriage calibration end comprehensively confirms the unloading rate curves of multiple groups of associated carriages in the same batch unloading process, confirms the average unloading speed corresponding to the corresponding associated carriage based on the different unloading rate curves associated with different associated carriages, and selects the sampling carriage from several groups of associated carriages based on the different average unloading speeds associated with different associated carriages; The associated data analysis end, based on the calibrated sampling carriage, gives priority to confirming the unloading rate curve associated with this sampling carriage, and confirms its unloading average speed marking line 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 unloading average speed marking line, the maximum difference segment is locked, and based on the maximum difference segment, the feature value interval is locked, and the locked feature value interval is displayed through the display end.

2. A sampling carriage management system based on railway bulk grain according to claim 1, characterized in that: The data analysis and processing end generates the vehicle unloading rate curve in the following specific manner: Based on the generated cargo model, the vertical inflection point of the cargo compartment outside the cargo model is confirmed, and the associated angles generated between the points on both sides of the vertical inflection point and the current point are 90°. Based on the four groups of vertical inflection points determined above the cargo model, the plane where the four groups of vertical inflection points are located is calibrated as the reference plane; Confirm the initial state of the cargo model body, move the reference plane upward horizontally, and when the reference plane coincides with the highest point of the cargo model body in the initial state, calibrate the reference plane at the current position as the level plane; Record the vertical distances between different points in the horizontal plane and the upper surface of the cargo model in the initial state, then record the sum of several groups of vertical distances, calibrate the confirmed vertical distance sum as ZH, and then based on the real-time changing cargo model body, confirm the vertical distance sum associated in real time, and based on the relevant difference of the vertical distance sum between adjacent moments, generate a difference rate curve about this cargo model body, and calibrate this difference rate curve as the unloading rate curve of this car box, where the relevant difference = the vertical distance sum at the previous moment - the vertical distance sum at the next moment.

3. A sampling carriage management system based on railway bulk grain according to claim 1, characterized in that: The specific method of selecting the sampling carriage at the sampling carriage calibration end is: Based on the different unloading rate curves associated with different associated carriages in this batch, the different unloading rates corresponding to different times are confirmed from the corresponding unloading rate curves, several groups of unloading rates are averaged to lock the average unloading rate of this associated carriage, and the average unloading rates of other associated carriages are confirmed in turn; The different unloading average speeds associated with different associated carriages in this batch are calibrated as J i , where i represents different associated carriages, and the average unloading speed J of several groups i Then perform mean processing to lock the eigenvalue Tz; Will satisfy: |J i The associated carriage with -Tz|≥Y1 is calibrated as the sampling carriage, where Y1 is the preset value.

4. A sampling carriage management system based on railway bulk grain according to claim 3, characterized in that: For dissatisfaction: |J i -For the associated carriages with Tz|≥Y1, no calibration is performed.

5. The railway bulk grain sampling carriage management system according to claim 1 is characterized in that: The specific method of locking the maximum difference segment at the associated data analysis end is: Based on the unloading rate curve associated with the corresponding sampling car box and the unloading average speed J associated with this unloading rate curve i , determine the average unloading speed J in the coordinate system where the unloading speed curve is located i The associated standard horizontal line, which is parallel to the horizontal axis of this coordinate system; Determine a set of line segment length intervals as [X1, X2], where X1 and X2 are preset values, representing the minimum length and maximum length of the corresponding selected line segment; The length of the selected line segment is limited to X1, and the first group of selection processes is executed: starting from the initial point of the unloading rate curve, the line segments are selected step by step, the line segment points inside the selected line segment are confirmed, the vertical distances between several line segment points and the standard horizontal line are determined, and several groups of vertical distances are summed to determine the line segment characteristics of the selected line segment; After the line segment associated with the initial point is selected, the selected line segments associated with the subsequent points are confirmed in turn, and the line segment features of the corresponding selected line segments are confirmed simultaneously, and so on, 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 [X1, X2], and subsequent different selection processes are executed. The selection method of each different selection process is consistent with the first group of selection processes, but the lengths of the selected line segments associated with different selection processes are different; From several groups of different selection processes, different line segment features associated with several groups of different selected line segments are identified, and from several groups of line segment features, the line segment feature with the maximum value is selected as the standard feature, and the selected line segment associated with the standard feature is used as the maximum difference segment.

6. A sampling carriage management system based on railway bulk grain according to claim 5, characterized in that: The specific method of locking the feature value interval from the maximum difference segment by the associated data analysis end is: Based on the determined maximum difference segment, the first moment and the last moment associated with the beginning and the end of the maximum difference segment are locked, and several groups of unloading rates associated with several groups of moments at the front end of the first moment are summed up to confirm the total rate V1 of the first moment, and V1×unit time=first feature is adopted to confirm the first feature associated with the first moment, wherein the unit time is a preset time, and several groups of unloading rates associated with several groups of moments at the front end of the last moment are summed up to confirm the total rate V2 of the last moment, and V2×unit time=last feature is adopted to confirm the last feature associated with the last moment; A set of feature value intervals is generated based on the first feature and the last feature confirmed by the maximum difference segment.

7. A sampling carriage management system based on railway bulk grain according to claim 6, characterized in that: The display terminal displays the determined feature value interval in a relevant manner.

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