A steel parts tracking method without process motion interference

Abstract

The present invention discloses a steel tracking method without process motion interference, which belongs to the field of steel rolling technology and solves the technical problem of misjudgment of steel tracking caused by abnormal detection elements or production interruptions. The method is to treat the steel transmission process as a black box and only focus on the consistency detection of the import and export quantities of steel parts. By memorizing the import steel part signals and comparing them with the real-time export steel part signals to complete their identity detection, steel tracking is achieved through identity identification. The present invention only focuses on the import and export steel part quantities, which can reduce the dependence on detection elements in process motion, reduce the probability of steel tracking failure, do not need to rely on the time sequence tracking of the process, avoid steel tracking failure during production interruptions, and improve the accuracy of steel tracking.

Description

Technical Field

[0001] The present invention relates to the technical field of steel rolling, and more particularly to a steel piece tracking method without process motion interference. Background Art

[0002] Current steel tracking technology in rolling mills is based on the time-sequential process flow and spatially sequenced detection elements. For example, tracking a steel part from the first set of rollers to the second set of rollers demonstrates that the part is detected by the detection elements on the first set of rollers at a timestamp of S seconds. As the first set of rollers advances, the part is detected by the detection elements on the second set of rollers at a timestamp of S+X seconds. From S seconds to S+X seconds, the part is detected by detection elements at different spatial locations on the first and second sets of rollers. This process, which meets a certain process sequence, is then validated to determine whether the part on the first set of rollers at S seconds and on the second set of rollers at S+X seconds is the same part. This identity verification allows for tracking of the billet.

[0003] The current steel parts tracking method has the following two defects:

[0004] This process tracking relies on the perfect performance of every detection component. If any component's signal is abnormal, steel tracking will fail. The production line in a steel rolling mill spans a long distance, involving a large number of detection components, which increases the probability of steel tracking failure.

[0005] 2. When production is suddenly interrupted and steel parts are left on a certain section of the production line for too long, they will be detected by the second set of rollers within S+X seconds, but they are actually detected by the second set of rollers within S+X+Y seconds. The time sequence of the steel parts will deviate from the legality of the normal process agreement, resulting in steel part tracking failure. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to address the above-mentioned deficiencies in the prior art, and the purpose of the present invention is to provide a steel part tracking method without process motion interference.

[0007] The technical solution of the present invention is: a steel parts tracking method without process action interference, which regards the steel parts transmission process as a black box and only focuses on the consistency detection of the import and export quantities of steel parts. The import steel parts signal is memorized and compared with the real-time signal of the export steel parts to complete its identity detection, and the steel parts tracking is realized through identity identification.

[0008] As a further improvement, the specific steps are as follows:

[0009] Step 1: Identify and mark the inlet steel parts. The inlet solid detection sensor identifies the inlet steel parts and assigns a unique virtual code to the steel parts to identify the uniqueness of the steel parts.

[0010] Step 2: Count the number of inlet steel parts, collect the pulse signal generated by the solid detection sensor when the inlet steel parts are identified, and trigger a cumulative count through the rising edge signal of the pulse signal;

[0011] Step 3: Storing the incoming steel parts to form a memory signal. The memory signal is used as a comparison object for the identity detection when the outgoing steel parts are detected.

[0012] Step 4: Determine the increase or decrease of steel parts in the process. There may be abnormalities in the process from the entrance to the exit. Steel parts with abnormalities cannot complete the process from the entrance to the exit. When this abnormality occurs, the number of steel parts at the entrance is greater than the number of steel parts at the exit. The exit steel part signal and the number of steel parts at the exit will be misaligned. The steel part abnormality determination program can correct this signal misalignment.

[0013] Step 5: Make statistics on the increase and decrease of process steel parts, identify abnormal steel parts in the process, and make real-time statistics on the number of abnormal steel parts;

[0014] Step 6: Real-time identification of the exported steel parts is performed through the export solid detection sensor, and the continuous steel signal needs to be processed on the falling edge;

[0015] Step 7: Compensate for process variations on the exported steel parts. If a steel part is rejected due to an abnormality, this will cause a misalignment in the export steel part signal. A compensation trigger signal needs to be generated before the steel part is detected at the export port.

[0016] Step 8: Statistics are taken for exported steel parts. The initial data for export steel parts statistics is accumulated by triggering the falling edge signal formed by export steel parts identification. After the initial data is obtained, it is saved. After checking whether compensation is triggered, if it is triggered, the compensation data and the initial data are accumulated to form the final export steel parts statistics value.

[0017] Step 9: Determine the identity of the exported steel parts and the imported steel parts by counting the exported steel parts and matching them with the order of the imported steel parts to identify and track the current steel parts;

[0018] Step 10: Identify and track the steel parts. After checking the identity of the steel parts, bind the corresponding virtual code to the steel parts to complete the steel part tracking.

[0019] Furthermore, in step one, the signal of the inlet solid detection sensor is filtered.

[0020] Furthermore, in step six, the signal of the outlet solid detection sensor is filtered.

[0021] Furthermore, the filtering process is specifically as follows: before the sensor signal is connected to the I / O module, a signal isolator is added to improve the signal's anti-interference ability; after connecting to the I / O module, the collected signal is chopped and anti-interference processing is performed on the signal at the software level.

[0022] Furthermore, the virtual code includes information on the steel part's entry time, temperature at entry, length, and weight.

[0023] Beneficial effects

[0024] Compared with the prior art, the present invention has the following advantages:

[0025] 1. The present invention performs anti-interference processing on the detected steel signal by adding a signal isolator and a filtering program, thereby improving the accuracy and stability of the steel signal.

[0026] 2. The present invention designs a unique virtual code for each steel piece so that each steel piece has its own unique identity.

[0027] 3. The present invention compensates for the misalignment of the exit steel part signal caused by the abnormal process steel part signal, thereby preventing the abnormal process signal from interfering with the steel part tracking.

[0028] 4. The present invention only focuses on the number of inlet and outlet steel parts, reducing the dependence on detection elements in process movement and reducing the probability of steel part tracking failure.

[0029] 5. The present invention does not need to rely on the time sequence tracking of the process, thus avoiding the failure of steel part tracking when production is interrupted and improving the accuracy of steel part tracking. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the present invention;

[0031] Figure 2 It is a specific flow chart of the present invention. DETAILED DESCRIPTION

[0032] The present invention will be further described below with reference to the specific embodiments in the accompanying drawings.

[0033] See Figures 1 and 2 A steel parts tracking method without process action interference treats the steel parts transmission process as a black box and only focuses on the consistency detection of the import and export quantities of steel parts. The import steel parts signal is memorized and compared with the real-time export steel parts signal to complete its identity detection, and the steel parts tracking is realized through identity identification.

[0034] The specific steps are as follows: Step 1 to Step 10:

[0035] Step 1: Identify and mark the inlet steel parts. The inlet solid detection sensor identifies the inlet steel parts and assigns a unique virtual code to the steel parts after identification to identify the uniqueness of the steel parts.

[0036] In order to avoid interference from false sensor signals, the sensor signal is filtered and the action state of the transmission equipment is linked, that is, the sensor signal + the action state of the equipment are used to simultaneously judge the entrance steel parts, so as to improve the accuracy and stability of the sensor signal and ensure the stability of the entrance steel parts identification signal.

[0037] The filtering process is specifically as follows: before the sensor signal is connected to the PLC I / O module, a signal isolator is added to improve the signal's anti-interference ability; after connecting to the I / O module, the collected signal is chopped and anti-interference processing is performed on the identified signal at the software level.

[0038] Step 2: Count the number of incoming steel parts. The PLC collects the pulse signal generated by the solid detection sensor when the incoming steel parts are identified, and triggers a cumulative count through the rising edge signal of the pulse signal.

[0039] The signal generated by the sensor when the steel part is identified is continuous (greater than 20 seconds). The PLC program is executed in a cycle (50ms). Under the triggering of this continuous signal, the same steel part will be counted multiple times by the PLC program (20*1000ms / 50ms), resulting in miscounting. By processing the rising edge of the signal, a continuous signal is optimized into a pulse signal, which is only executed once by the PLC program to ensure accurate counting.

[0040] Step 3: Storing the incoming steel parts. Storing the incoming steel parts to form a memory signal. The memory signal is used as a comparison object for identity detection when the outgoing steel parts are detected.

[0041] Specifically, due to the time difference between the entrance and the exit, the steel parts at the entrance and exit in the same time period are not the same steel parts. In order to ensure that there are comparable objects when the steel parts are detected at the exit, the steel parts at the entrance must be stored to form a memory signal. The memory signal will be used as a comparison object for identity detection when the steel parts at the exit are detected.

[0042] The memory signal is specifically: the signals collected by the PLC's I / O module are stored in its mapping area in real time. The data in the signal mapping area will be updated in each collection cycle. In order to ensure that the signals of the past cycle are not overwritten by the signals of the new cycle, the signals of the current cycle need to be memorized through the program to form a memory signal.

[0043] Step 4: Determine the increase or decrease of process steel parts. There may be some abnormal conditions such as scrap removal, steel piling, rotten steel, etc. from the entrance to the exit. That is, there may be abnormal conditions from the entrance to the exit. Steel parts with abnormal conditions cannot complete the process from the entrance to the exit. When this abnormal situation occurs, the number of entrance steel parts is greater than the number of exit steel parts, and the exit steel parts signal and the number of exit steel parts will be misaligned. This signal misalignment can be corrected through the steel parts abnormality determination program.

[0044] For example, steel parts A, B, C, D, and E are detected at the entrance at time points T1, T2, T3, T4, and T5, respectively. Steel part B is scrapped at time T6, the first steel part is detected at the exit at time T7, and the fourth steel part is detected at the exit at time T11. The order of steel parts at the entrance is A, B, C, D, E, and the theoretical order of steel parts at the exit is A, B, C, D, E. However, due to the scrapping of steel part B, the actual exit order is A, C, D, E. Therefore, C, D, and E are all misalignment signals.

[0045] Step 5: Make statistics on the increase and decrease of process steel parts, identify abnormal steel parts in the process, and make real-time statistics on the number of abnormal steel parts.

[0046] Since the influence of the number of abnormal steel parts in the process on whether the export steel part signal is misaligned is real-time, the counting of abnormal steel parts does not require memory processing.

[0047] Step 6: Real-time identification of exiting steel parts. The exiting solids detection sensor identifies exiting steel parts and performs falling-edge processing on the continuous steel presence signal. The exiting solids detection sensor signal needs to be filtered, meaning the signal needs to undergo the same anti-interference processing as the inlet steel part signal. Falling-edge processing involves collecting and storing the signal's transition from 1 to 0 to generate a new pulse signal.

[0048] Step 7: Compensate for process changes in the exported steel parts. The process steel parts are rejected due to abnormalities, resulting in misalignment of the export steel part signals. A compensation trigger signal needs to be generated before the steel parts are detected at the export.

[0049] There is a fixed spatial process between the steel parts from the entrance to the exit. The steel signal at the entrance needs to go through a certain delay to form the steel signal at the exit. This identical delay is the process steel parts.

[0050] Step 8: Statistics are taken for exported steel parts. The initial data for statistics of exported steel parts is accumulated by triggering the falling edge signal formed by export steel parts identification. After the initial data is obtained, it is saved and checked to see if compensation is triggered. If triggered, the compensation data and the initial data are accumulated to form the final statistical value of exported steel parts.

[0051] The final value takes into account the steel parts that were rejected by the process and is used to correct the misalignment signal.

[0052] Step 9: Determine the identity of the exported steel parts and the imported steel parts. By counting the exported steel parts and matching them with the order of the imported steel parts, the identity can be determined to identify and track the current steel parts.

[0053] If the import steel part count is 20 and the export steel part final count is 7, then the 7th import steel part and the current export steel part are identical.

[0054] Identity refers to the same object at different times and places.

[0055] Step 10: Identify and track the steel parts. After checking the identity of the steel parts, bind the corresponding virtual code to the steel parts to complete the steel part tracking.

[0056] In this embodiment, the virtual code includes information on the steel piece's entry time, temperature, length, and weight at the time of entry.

[0057] The above is only a preferred embodiment of the present invention. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the structure of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. A steel tracking method without process motion interference, characterized in that: Treat the steel parts transmission process as a black box, focusing only on the consistency detection of the import and export quantities of steel parts. By memorizing the import steel part signal and comparing it with the real-time export steel part signal to complete its identity detection, steel parts tracking is achieved through identity identification. The specific steps are as follows: Step 1: Identify and mark the inlet steel parts. The inlet solid detection sensor identifies the inlet steel parts and assigns a unique virtual code to the steel parts to identify the uniqueness of the steel parts. Step 2: Count the number of inlet steel parts, collect the pulse signal generated by the solid detection sensor when the inlet steel parts are identified, and trigger a cumulative count through the rising edge signal of the pulse signal; Step 3: Storing the incoming steel parts to form a memory signal. The memory signal is used as a comparison object for the identity detection when the outgoing steel parts are detected. Step 4: Determine the increase or decrease of steel parts in the process. There may be abnormalities in the process from the entrance to the exit. Steel parts with abnormalities cannot complete the process from the entrance to the exit. When this abnormality occurs, the number of steel parts at the entrance is greater than the number of steel parts at the exit. The exit steel part signal and the number of steel parts at the exit will be misaligned. The steel part abnormality determination program can correct this signal misalignment. Step 5: Make statistics on the increase and decrease of process steel parts, identify abnormal steel parts in the process, and make real-time statistics on the number of abnormal steel parts; Step 6: Real-time identification of the exported steel parts is performed through the export solid detection sensor, and the continuous steel signal needs to be processed on the falling edge; Step 7: Compensate for process variations on the exported steel parts. If a steel part is rejected due to an abnormality, this will cause a misalignment in the export steel part signal. A compensation trigger signal needs to be generated before the steel part is detected at the export port. Step 8: Statistics are taken for exported steel parts. The initial data for export steel parts statistics is accumulated by triggering the falling edge signal formed by export steel parts identification. After the initial data is obtained, it is saved. After checking whether compensation is triggered, if it is triggered, the compensation data and the initial data are accumulated to form the final export steel parts statistics value. Step 9: Determine the identity of the exported steel parts and the imported steel parts by counting the exported steel parts and matching them with the order of the imported steel parts to identify and track the current steel parts; Step 10: Identify and track the steel parts. After checking the identity of the steel parts, bind the corresponding virtual code to the steel parts to complete the steel part tracking.

2. The steel tracking method without process motion interference according to claim 1 is characterized in that: In step one, the signal of the inlet solid detection sensor is filtered.

3. The steel tracking method without process motion interference according to claim 1, characterized in that: In step six, the signal of the outlet solid detection sensor is filtered.

4. A steel tracking method without process motion interference according to claim 2 or 3, characterized in that: The filtering process is specifically as follows: before the sensor signal is connected to the I / O module, a signal isolator is added to improve the signal's anti-interference ability. After connecting to the I / O module, the collected signal is chopped and anti-interference processing is performed on the signal at the software level.

5. The steel part tracking method without process motion interference according to claim 1, characterized in that: The virtual code contains information about the steel part's entry time, temperature at entry, length, and weight.

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