Strip steel production data monitoring method and related equipment

Abstract

The invention discloses a strip steel production data monitoring method and related equipment, relates to the field of strip steel production, and mainly aims to solve the problem of inaccurate tracing of the strip steel production data at present. The method comprises the following steps: dividing a production line into a preset number of physical areas; a machining time window is determined based on the head loading moment and the tail unloading moment when the strip steel enters the target physical area; and obtaining process parameters in the processing time window to form a data packet. The method is used for the strip steel production data monitoring process.

Description

Technical Field

[0001] The present invention relates to the field of strip steel production, and in particular to a strip steel production data monitoring method and related equipment. Background Art

[0002] In strip processing, steel strips pass through rolling mills, shears, cooling layers, and coiling processes at a specific speed. These processes require real-time monitoring, including data such as the rolling force, roll gap, and temperature fluctuations of the individual strips. Inaccurate data collection can lead to difficulties in tracing quality issues such as uneven strip thickness and surface cracks. Traditional data processing systems typically rely on two approaches: Method 1: Data collection based on "head time." This assumes that the arrival time of the strip's head (front end) when it enters a certain piece of equipment is used as the starting point for data collection. However, due to long strips, the tail (back end) may still be rolling even after data collection has stopped, resulting in data loss. Method 2: Data is collected at fixed time intervals, such as every 1 second. However, if the strip's rolling time at a particular piece of equipment increases (for example, due to changes in production speed), fixed time slicing will split a continuous data segment into several segments, resulting in incomplete data. Consequently, current strip production data traceability is inaccurate. Summary of the Invention

[0003] In view of the above problems, the present invention provides a strip steel production data monitoring method and related equipment, the main purpose of which is to solve the problem that the current strip steel production data tracing is not accurate enough.

[0004] To solve at least one of the above technical problems, in a first aspect, the present invention provides a method for monitoring strip steel production data, the method comprising:

[0005] Divide the production line into a preset number of physical areas;

[0006] Determine the processing time window based on the head loading moment and tail unloading moment of the strip entering the target physical area;

[0007] The process parameters within the processing time window are acquired to form a data packet.

[0008] Optionally, dividing the production line into a preset number of physical areas includes:

[0009] Generate initial zone configuration based on actual layout of rolling mill equipment;

[0010] A material tracking signal of the steel strip is obtained to update the initial area configuration and form a physical area.

[0011] Optionally, the above method further includes:

[0012] The moment when the head of the strip enters the target physical area is recorded as the head loading moment.

[0013] The moment when the tail of the strip enters the target physical area is recorded as the tail unloading moment. Optionally, the method further includes:

[0014] determining an overlapping scenario when a second head loading moment of a second target physical zone of the steel strip is later than a first tail unloading moment of the first target physical zone, wherein the steel strip first enters the first target physical zone and then enters the second target physical zone;

[0015] In the case of overlapping scenarios, processing time window cutting and process parameter redistribution are performed.

[0016] Optionally, when there are overlapping scenarios, cutting the processing time window and redistributing the process parameters include:

[0017] The recorded first tail unloading moment is flexibly extended to supplement process compensation time for the first target physical area, wherein the process compensation time serves as a buffer between the first target physical area and the second target physical area.

[0018] Optionally, when there are overlapping scenarios, performing processing time window cutting and process parameter redistribution includes:

[0019] The process parameters in the overlapping scenario are written into the first target physical area and the second target physical area respectively.

[0020] Optionally, the above method further includes:

[0021] Generate multi-dimensional parameter labels based on process parameters;

[0022] The multi-dimensional parameter tag and the processing time window of the target physical area are bound to form an area parameter joint index table.

[0023] In a second aspect, an embodiment of the present invention further provides a strip steel production data monitoring device, comprising:

[0024] A division unit is used to divide the production line into a preset number of physical areas;

[0025] a determination unit, configured to determine a processing time window based on a head loading moment and a tail unloading moment of the strip entering a target physical area;

[0026] The forming unit is used to obtain the process parameters within the processing time window to form a data packet.

[0027] In order to achieve the above-mentioned purpose, according to the third aspect of the present invention, a computer-readable storage medium is provided, which includes a stored program, wherein when the above-mentioned program is executed by a processor, the steps of the above-mentioned strip production data monitoring method are implemented.

[0028] In order to achieve the above-mentioned purpose, according to the fourth aspect of the present invention, an electronic device is provided, comprising at least one processor and at least one memory connected to the processor; wherein the above-mentioned processor is used to call the program instructions in the above-mentioned memory to execute the steps of the above-mentioned strip production data monitoring method.

[0029] By means of the above technical solution, the strip production data monitoring method and related equipment provided by the present invention solve the problem that the current traceability of strip production data is not accurate enough. The present invention divides the production line into a preset number of physical areas; determines the processing time window based on the head loading moment and the tail unloading moment when the strip enters the target physical area; obtains the process parameters within the processing time window to form a data packet. In the above solution, the production line is divided into multiple physical areas, and the time period for data collection is dynamically adjusted according to the time when the strip actually enters and leaves each area. Since the spatial position and time of the strip are considered at the same time to collect data, a spatiotemporal mapping relationship between the strip and the equipment is established. If a quality problem occurs in a certain section of the strip, the spatiotemporal data can be used to quickly locate which equipment and which time period the problem is, and the tail data will not be missed, nor will the data be cut incorrectly due to changes in production speed. Even if the equipment layout of the production line is adjusted, the system can dynamically update the area division without the need for reprogramming.

[0030] Correspondingly, the strip steel production data monitoring device, equipment and computer-readable storage medium provided by the embodiments of the present invention also have the above-mentioned technical effects.

[0031] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0033] Figure 1 A schematic flow chart of a strip steel production data monitoring method provided by an embodiment of the present invention is shown;

[0034] Figure 2 A schematic block diagram of the composition of a strip steel production data monitoring device provided by an embodiment of the present invention is shown;

[0035] Figure 3 A schematic block diagram of the composition of an electronic device for monitoring strip steel production data provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0036] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0037] In order to solve the problem that the current strip steel production data tracing is not accurate enough, the embodiment of the present invention provides a strip steel production data monitoring method, such as Figure 1 As shown, the method includes:

[0038] S101, dividing the production line into a preset number of physical areas;

[0039] S102, determining a processing time window based on the loading moment of the head portion and the unloading moment of the tail portion of the strip entering the target physical area;

[0040] S103: Acquire process parameters within the processing time window to form a data packet.

[0041] For example, in this application, each physical area corresponds to a functional module in the rolling process. The head loading time and tail unloading time can be determined by using a hot metal detector arranged at the rolling mill entrance to capture the signal when the strip head enters or leaves the target area.

[0042] The processing time window described above essentially refers to the duration during which the strip material completely covers the equipment's working surface. The data package contains a collection of three-dimensional process parameters, including those from pressure sensors (roll gap gauges), temperature sensors (infrared thermometers), and speed encoders (main motor speed gears) during this time period. Spatial discretization enables spatiotemporal alignment of process parameters, eliminating the mismatch between traditional timestamp data and physical locations and providing accurate operating boundary conditions for subsequent big data analysis.

[0043] For example, in a pickling mill, the production line between the uncoiler and the coiler is divided into 12 physical zones (including the pickling tank and the five-stand tandem mill). When the strip head enters the fifth stand, a timer, T_start, is triggered; when the strip tail leaves the fifth stand, T_end is triggered. Data such as rolling force and roll gap oil film thickness are collected within the interval [T_start, T_end].

[0044] By leveraging the aforementioned technical solution, the present invention divides the production line into multiple physical zones and dynamically adjusts the data collection time period based on the actual time the strip enters and leaves each zone. Because data collection considers both the strip's spatial position and time, a spatiotemporal mapping relationship between the strip and the equipment is established. If a quality issue arises in a particular section of the strip, the spatiotemporal data can be used to quickly pinpoint the issue to the device and time period, without missing any tail data or mis-slicing data due to changes in production speed. Even if the production line adjusts its equipment layout, the system can dynamically update the zone divisions without requiring reprogramming.

[0045] In one embodiment, dividing the production line into a preset number of physical areas includes:

[0046] Generate initial zone configuration based on actual layout of rolling mill equipment;

[0047] A material tracking signal of the steel strip is obtained to update the initial area configuration and form a physical area.

[0048] For example, the initial zone configuration is generated based on the equipment topology, specifically by converting CAD coordinates from the production line design drawings. The center point of each equipment node serves as the initial reference for zone boundaries. The aforementioned material tracking signals come from a laser velocimeter (installed on the trackside of the looper) and a weld detector (gamma-ray flaw detector). When a strip weld crosses a zone boundary, the zone configuration is updated. The update algorithm uses a sliding window filter to process the displacement sensor pulse signal.

[0049] For example, the initial zone length is set to 3.2 meters based on the spacing between the rolling mill arches. When the strip deviates in a serpentine manner due to tension fluctuations, the zone width is dynamically adjusted by ±15 cm using edge detection camera data.

[0050] With the help of the above technical solution, the physical deformation error of the strip during operation can be dynamically compensated to ensure that the process parameter collection area accurately corresponds to the actual processing position.

[0051] In one embodiment, the method further includes:

[0052] The moment when the head of the strip enters the target physical area is recorded as the head loading moment.

[0053] The moment when the tail of the strip enters the target physical area is recorded as the tail unloading moment.

[0054] The above-mentioned head loading moment is used to characterize the starting time when the head of the strip contacts the roll and forms an effective rolling force. "Loading" is an abbreviation of "carrying load", referring to the critical state when the roll system of the rolling mill begins to bear the pressure of the strip. It can also be understood as the "biting moment" (when the head of the strip enters the roll) or the "starting point of loading". The above-mentioned tail unloading moment is used to characterize the critical time node when the tail of the strip disengages from a certain device or process area and stops bearing the load during the strip production process. When the tail of the strip completely disengages from a certain device (such as a rolling mill, coiler), the process loads (such as rolling force, tension, temperature control) of the device on the strip will terminate, and this moment is the tail unloading moment.

[0055] With the above technical solution, a strict correspondence relationship between the strip entity and the device action time is established, solving the cumulative error problem caused by virtual tracking in the traditional method.

[0056] In one embodiment, the above method further includes:

[0057] In the case where the second head loading moment of the second target physical area of the strip is later than the first tail unloading moment of the first target physical area, an overlapping scenario is determined, where the strip first enters the first target physical area and then enters the second target physical area;

[0058] In the case of an overlapping scenario, the processing time window is cut and the process parameters are redistributed.

[0059] Taking the rough rolling area (R1) and the finish rolling area (R2) as an example:

[0060] Physical space relationship: The two areas are connected by a roller table, and the distance is L (for example, 50 meters)

[0061] Strip motion state: Strip length S = 200m, rolling speed v = 10m / s

[0062] Time window calculation:

[0063] R1 processing time window: T1_start (head enters the rough rolling mill) → T1_end (tail leaves the rough rolling mill)

[0064] R2 processing time window: T2_start (head enters the finish rolling mill) → T2_end (tail leaves the finish rolling mill)

[0065] When T2_start < T1_end (that is, the time when the head enters the finish rolling mill is earlier than the time when the tail leaves the rough rolling mill), the time windows of the two areas overlap.

[0066] Exemplarily, the overlap detection logic is based on the temporal and spatial relationship between the two physical regions. When the loading moment of the head of the second target physical region is earlier than the unloading moment of the tail of the first target physical region, an overlap is determined. Processing time windows are then cut and process parameters are reallocated.

[0067] This technical solution solves the problem of overlapping processing time windows when the strip passes through adjacent physical zones. It eliminates the ambiguity in the attribution of process parameters across zones, ensures the spatiotemporal integrity of data packages within each physical zone, and provides accurate local operating condition data for process optimization.

[0068] In one embodiment, when overlapping scenes exist, performing processing time window cutting and process parameter reallocation includes:

[0069] The recorded first tail unloading moment is flexibly extended to supplement process compensation time for the first target physical area, wherein the process compensation time serves as a buffer between the first target physical area and the second target physical area.

[0070] Continuing with the above rough rolling area (R1) and finishing rolling area (R2) as an example, the above processing time window cutting is:

[0071] The effective end time of R1: adjusted to T1_end'=T2_start+Δt

[0072] R2 effective start time: maintain T2_start unchanged

[0073] Δt: process compensation time

[0074] For example, the elastic extension mechanism dynamically adjusts the time window boundaries through process compensation time. By setting a hysteresis coefficient for the tail unloading moment of the first target physical zone in the PLC, the compensation time length can be dynamically calculated based on strip thickness, tension fluctuations, the distance between adjacent equipment zones, and the strip elastic modulus. Typical values ​​are 0.2-0.5 seconds. The process parameters in the buffer zone can be estimated using a Kalman filter and added to the end of the original time window.

[0075] This technical solution forcibly assigns overlapping time periods to the previous region (R1), preventing duplicate attribution of data at the same moment. The introduction of Δt compensates for the elastic deformation of the strip's tail during roller conveyance. Dynamic time windowing ensures the physical continuity of the strip's head and tail motions, ensuring that artificial divisions are not disrupted. This particularly protects the mechanical properties of the strip's midsection in the transition zone.

[0076] It is understood that this application also includes overlap checking and warning:

[0077] When R2.Ts_start < R1.Ts_end - Δt_max (Δt_max is the maximum allowable overlap threshold, such as 3 seconds), the following processing is triggered: freeze the calculation of the subsequent area time window, start the recalibration of the strip tracking system, and record the original data of the overlapping period to the buffer for subsequent repair.

[0078] In one embodiment, in the case of an overlapping scenario, performing processing time window cutting and process parameter reallocation includes:

[0079] Write the process parameters in the overlapping scenario to the first target physical area and the second target physical area respectively.

[0080] Continuing with the rough rolling area (R1) and the finish rolling area (R2) as an example: <l

[0081] Strip length: 150m, rough rolling area length: 30m, distance from the entrance of the finish rolling area to the exit of the rough rolling area: 20m, rolling speed: 12m / s

[0082] The theoretical time window is:

[0083] R1: T1_start = 0s → T1_end = (30 + 150) / 12 = 15s

[0084] R2: T2_start = (20) / 12 ≈ 1.67s → T2_end = (20 + 150) / 12 ≈ 14.17s

[0085] When it is detected that T2_start (1.67s) < T1_end (15s) → there is an overlap of 13.5 seconds, dynamic adjustment is performed:

[0086] R1_end' = T2_start + Δt = 1.67 + 0.3 = 1.97s, R2_start remains 1.67s

[0087] Thus, the data in the period of 1.67s to 1.97s is written to both R1 and R2 through weight allocation; the data after 1.97s completely belongs to R2.

[0088] By means of the above technical solution, E1: the head of the strip enters R2 (triggering data acquisition in the finish rolling area), E2: the tail of the strip leaves R1 (closing the data stream in the rough rolling area). During the overlapping period between E1 and E2, the system enables dual-channel data mirroring and writes data copies to the storage partitions of both R1 and R2. The accurate determination of the physical attribution of overlapping parameters is achieved, avoiding the feature aliasing problem caused by the traditional time averaging method.

[0089] In one embodiment, the above method further includes:

[0090] Generate multi-dimensional parameter labels based on process parameters;

[0091] The multi-dimensional parameter tag and the processing time window of the target physical area are bound to form an area parameter joint index table.

[0092] Exemplarily, the above-mentioned multi-dimensional parameter tags include: process dimension: rolling stage code, cooling mode identification, equipment dimension: bearing seat number, transmission side identification, quality dimension: surface defect grade code, etc.

[0093] The regional parameter joint index table of this application adopts a spatiotemporal quadruple structure (region ID, start timestamp, end timestamp, label hash value) and establishes an inverted index in the HBase database, thereby realizing multi-condition combination retrieval of massive working condition data.

[0094] Furthermore, as a response to the above Figure 1 In order to realize the method shown in the figure, the embodiment of the present invention also provides a strip steel production data monitoring device for monitoring the above Figure 1 This device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this device embodiment will not describe the details of the aforementioned method embodiment one by one, but it should be clear that the device in this embodiment can implement all the contents of the aforementioned method embodiment. Figure 2 As shown, the device includes: a dividing unit 21, a determining unit 22, and a forming unit 23, wherein

[0095] A division unit 21 is used to divide the production line into a preset number of physical areas;

[0096] A determination unit 22, configured to determine a processing time window based on a head loading moment and a tail unloading moment of the strip entering a target physical area;

[0097] The forming unit 23 is configured to obtain the process parameters within the processing time window to form a data packet.

[0098] The processor includes a core, which retrieves the corresponding program unit from the memory. One or more cores can be provided, and by adjusting the core parameters, a strip steel production data monitoring method is implemented, which can solve the current problem of inaccurate strip steel production data traceability.

[0099] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program. When the program is executed by a processor, the strip steel production data monitoring method is implemented.

[0100] An embodiment of the present invention provides a processor, which is used to run a program, wherein the strip steel production data monitoring method is executed when the program is run.

[0101] An embodiment of the present invention provides an electronic device, comprising at least one processor and at least one memory connected to the processor; wherein the processor is configured to call program instructions in the memory to execute the strip steel production data monitoring method as described above.

[0102] An embodiment of the present invention provides an electronic device 30, such as Figure 3 As shown, the electronic device includes at least one processor 301, and at least one memory 302 and a bus 303 connected to the processor; wherein the processor 301 and the memory 302 communicate with each other through the bus 303; the processor 301 is used to call the program instructions in the memory to execute the above-mentioned strip production data monitoring method.

[0103] The intelligent electronic devices in this article can be PCs, PADs, mobile phones, etc.

[0104] The present application also provides a computer program product, which, when executed on a process management electronic device, is suitable for executing a program initialized with the steps of the above-mentioned strip steel production data monitoring method.

[0105] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0106] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0107] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0108] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0110] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 This corresponds to the flow of memory control in the embodiment.

[0111] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, a process or function according to an embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. A computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integrations. Available media may be magnetic media (e.g., floppy disk, hard disk, tape), optical media (e.g., DVD), or semiconductor media (e.g., solid-state disk (SSD)), etc.

[0112] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0114] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0115] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0116] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0117] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A strip steel production data monitoring method, characterized in that: include: Divide the production line into a preset number of physical areas; Determine the processing time window based on the head loading moment and tail unloading moment of the strip entering the target physical area; The process parameters within the processing time window are acquired to form a data packet.

2. The method according to claim 1, characterized in that The production line is divided into a preset number of physical areas, including: Generate initial zone configuration based on actual layout of rolling mill equipment; A material tracking signal of the steel strip is obtained to update the initial area configuration and form a physical area.

3. The method according to claim 1, characterized in that Also includes: The moment when the head of the strip enters the target physical area is recorded as the head loading moment. The moment when the tail of the steel strip enters the target physical area is recorded as the tail unloading moment.

4. The method according to claim 1, wherein Also includes: determining an overlapping scenario when a second head loading moment of a second target physical zone of the steel strip is later than a first tail unloading moment of the first target physical zone, wherein the steel strip first enters the first target physical zone and then enters the second target physical zone; In the case of overlapping scenarios, processing time window cutting and process parameter redistribution are performed.

5. The method according to claim 4, characterized in that The process of cutting the processing time window and redistributing the process parameters in the presence of overlapping scenarios includes: The recorded first tail unloading moment is flexibly extended to supplement process compensation time for the first target physical area, wherein the process compensation time serves as a buffer between the first target physical area and the second target physical area.

6. The method according to claim 4, characterized in that The process of cutting the processing time window and redistributing the process parameters in the presence of overlapping scenarios includes: The process parameters in the overlapping scenario are written into the first target physical area and the second target physical area respectively.

7. The method according to claim 1, characterized in that Also includes: Generate multi-dimensional parameter labels based on process parameters; The multi-dimensional parameter tag and the processing time window of the target physical area are bound to form an area parameter joint index table.

8. A strip steel production data monitoring device, characterized in that: Also includes: A division unit is used to divide the production line into a preset number of physical areas; a determination unit, configured to determine a processing time window based on a head loading moment and a tail unloading moment of the strip entering a target physical area; The forming unit is used to obtain the process parameters within the processing time window to form a data packet.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed by a processor, the steps of the strip steel production data monitoring method according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: The electronic device includes at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the steps of the strip production data monitoring method according to any one of claims 1 to 7.

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