A data processing method, an electronic device, and a storage medium

Abstract

This application discloses a data processing method, electronic device, and storage medium. It involves acquiring a first data sequence from a reference measuring device and a second data sequence from a target measuring device, located at different positions on a target production line. The first data sequence includes several first data points, and the second data sequence includes several second data points. Based on the target distance between the reference and target measuring devices and historical production line speed information, target data points matching each of the first data points are determined from the second data points. The matched target data points are then aligned with the first data points. By combining the fluctuating production line speed and the distance between devices, the data collected by each device is aligned, thereby improving alignment accuracy.

Description

Technical Field

[0001] This application relates to the field of roll coating production technology, and in particular to a data processing method, electronic device and storage medium. Background Technology

[0002] On a continuous coil coating production line, product quality is determined by multiple heterogeneous devices distributed across different physical locations on the line. Among them, the Critical To Process (CTP) coating machine controls the slurry supply and coating process parameters; an online X-ray measuring device monitors areal density; and a charge-coupled device (CCD)-based detection system identifies surface defects and measures the coil's dimensional data. These three types of data respectively characterize the "process conditions—internal quality—appearance quality" of the same coil cross-section, serving as essential inputs for subsequent process optimization, quality traceability, and closed-loop control.

[0003] However, these devices are arranged in a "distributed" manner in space, and each uses an independent sampling clock and communication protocol, which causes the timestamps of the same roll cross section to be naturally misaligned when passing through different detection points; in addition, the production line speed fluctuates at the second level, and if the traditional "fixed delay + manual alignment" method is used, significant time drift error will be introduced.

[0004] Therefore, existing monitoring solutions for continuous roll coating production suffer from low accuracy in data alignment among the various devices. Summary of the Invention

[0005] Embodiments of this application provide a data processing method, electronic device, and storage medium that align data collected by various devices by taking into account the fluctuating production line speed, thereby improving alignment accuracy.

[0006] In a first aspect, embodiments of this application provide a data processing method, the method comprising: A first data sequence from a reference measuring device and a second data sequence from a target measuring device are acquired, wherein the reference measuring device and the target measuring device are located at different positions on the target production line, the first data sequence includes a plurality of first data points, and the second data sequence includes a plurality of second data points; Based on the target distance between the reference measuring device and the target measuring device, and the historical production line speed information of the target production line, target data points that match each of the first data points are determined from the second data points; The matched target data points are aligned with the first data point.

[0007] In one embodiment, the production line speed history information includes several production line speeds and the measurement time of each production line speed; determining the theoretical measurement time of the target measuring device at the corresponding measurement position of each first data point includes: For each of the first data points, starting from the first measurement time of the first data point, the target measurement time is selected multiple times according to the unit measurement time. Based on the production line speed corresponding to each of the target measurement times and the unit measurement time, the moving distance corresponding to each of the target measurement times is determined; Based on the moving distance, update the cumulative distance the target production line moves toward the target measuring device until the distance difference between the cumulative distance and the target distance is not greater than a preset distance difference. The target measurement time is taken as the theoretical measurement time of the target measurement device at the measurement position corresponding to the first data point.

[0008] In this way, dynamic delay backtracking can be performed by combining fluctuating production line speeds to truly reflect material transfer time, thereby improving the data alignment accuracy of different measuring devices.

[0009] In one embodiment, the above data processing method further includes: If the distance difference between the cumulative distance and the target distance is greater than the preset distance difference, and if the production line speed corresponding to the target measurement time is abnormal, and the duration of the abnormality is not less than the preset duration threshold, the target production line speed corresponding to the first measurement time of the first data point is obtained. Based on the target distance and the target production line speed, determine the delay time between the target measuring device and the reference measuring device; Based on the delay duration and the first measurement time of the first data point, the theoretical measurement time of the target measuring device at the measurement position corresponding to the first data point is determined.

[0010] Thus, by providing multiple methods for determining theoretical measurement times, such as dynamic delay and fixed delay, the accuracy of the determined theoretical measurement times can be improved, thereby increasing the accuracy of data alignment based on theoretical measurement times.

[0011] In one embodiment, the data processing method further includes: If there is no second data point in the second data sequence whose second measurement time falls within the time search range, update the range adjustment coefficient. Continue executing the step of determining the time search range of the target data point matching the first data point based on the sampling period of the target measuring device, the range adjustment coefficient of the sampling period, and the theoretical measurement time, wherein the updated range adjustment coefficient is greater than the original range adjustment coefficient.

[0012] In this way, precise matching can be performed first within a smaller time range; if the search fails, the time range restriction can be relaxed, and the search can be tried again within a larger time range to balance matching accuracy and matching success rate.

[0013] Secondly, embodiments of this application provide a data processing apparatus, the data processing apparatus comprising: The data acquisition module is used to acquire a first data sequence from a reference measuring device and a second data sequence from a target measuring device, wherein the reference measuring device and the target measuring device are set at different positions on the target production line, the first data sequence includes a plurality of first data points, and the second data sequence includes a plurality of second data points; The matching module is used to determine target data points that match each of the first data points from the second data points based on the target distance between the reference measuring device and the target measuring device, and the historical production line speed information of the target production line. An alignment module is used to align the matched target data point with the first data point.

[0014] Thirdly, embodiments of this application provide an electronic device, the electronic device comprising: One or more processors; Memory; and One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the steps in the data processing method of any one of the first aspects.

[0015] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to perform the steps of the data processing method described in any of the first aspects.

[0016] Fifthly, this application also provides a computer program product, including a computer program / instructions, which, when executed by a processor, are used to perform the steps in the data processing method described in any of the first aspects above.

[0017] The beneficial effects of the embodiments of this application are as follows: In the embodiments of this application, a first data sequence from a reference measuring device and a second data sequence from a target measuring device are acquired. The reference measuring device and the target measuring device are positioned at different locations on the target production line. The first data sequence includes several first data points, and the second data sequence includes several second data points. Based on the target distance between the reference measuring device and the target measuring device, and the historical production line speed information of the target production line, target data points matching each of the first data points are determined from the second data points. The matched target data points are then aligned with the first data points. Thus, by combining the fluctuating production line speed and the distance between the devices, the data collected by each device is aligned to improve alignment accuracy. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of an implementation scenario provided in the embodiments of this application; Figure 2 This is a flowchart illustrating the data processing method provided in the embodiments of this application; Figure 3 This is a schematic diagram of the measurement equipment setup on the target production line in the data processing method provided in this application embodiment; Figure 4 This is a schematic diagram illustrating the process of constructing a segmented mapping file and a distance configuration file for a target production line, as provided in this embodiment of the application. Figure 5 This is a flowchart illustrating the process of determining the theoretical measurement time in an embodiment of this application; Figure 6 This is an interpolation diagram illustrating the interpolation process provided in the embodiments of this application; Figure 7 This is a flowchart illustrating a specific embodiment of the data processing method provided in this application. Figure 8 This is another flowchart illustrating a specific embodiment of the data processing method provided in this application. Figure 9 This is a schematic diagram of an embodiment of the data processing apparatus provided in this application; Figure 10 This is a schematic diagram of an embodiment of the electronic device provided in this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. At the same time, in the description of the embodiments of this application, the terms "first," "second," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0021] A continuous coil coating production line typically consists of a coating machine, a drying section, a calendering unit, and multiple online inspection devices. To ensure product consistency and traceability, the production line is usually equipped with various types of inspection devices located in different physical locations. These include: coating machine process parameter acquisition devices (such as pump speed, coating amount, tension, etc.) for recording process conditions; online X-ray inspection devices for measuring the density after coating; and charge-coupled device (CCD) based vision inspection devices for acquiring information such as surface defects and width. These devices measure the same coil at different locations along the conveyor path, ensuring that the collected results reflect the process status, internal quality, and appearance quality of the coil cross-section.

[0022] Because various testing devices are distributed across the production line, each device has an independent sampling clock, data communication method, and triggering mechanism. Therefore, their data timestamps are independent of each other, resulting in natural time misalignment when different devices measure the same roll cross-section. Furthermore, the roll conveying speed may fluctuate by seconds due to factors such as operating conditions, tension adjustment, or short stops, significantly amplifying the material transport time error calculated based on fixed delays or a single speed assumption. Existing technologies often employ fixed time offsets, manual calibration, or simple proximity matching for cross-device data alignment. However, under actual operating conditions such as large speed fluctuations, different sampling frequencies, or the presence of instantaneous data gaps, these methods are prone to producing significant positional offset errors, making it difficult to accurately determine the correspondence between data collected by different devices.

[0023] Therefore, in the continuous coating production process of roll materials, how to accurately determine the measurement results of different testing equipment for the same roll material position, and achieve high-precision and traceable data alignment, while taking into account the transmission distance between equipment along the conveying direction, the dynamic changes in production line speed, and the differences in the sampling characteristics of multi-source data, has become a technical problem that urgently needs to be solved in this field.

[0024] To address at least some of the aforementioned problems, this application proposes a data processing method, apparatus, electronic device, storage medium, and computer program product. The data processing apparatus can be integrated into an electronic device, which can be a server, such as a data processing system, or a terminal controlled by the data processing system.

[0025] The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, network acceleration services (Content Delivery Network, CDN), as well as big data and artificial intelligence platforms.

[0026] The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and the server can be connected directly or indirectly through wired or wireless communication, which is not limited herein.

[0027] Please see Figure 1 Taking the integration of data processing devices into electronic devices as an example, Figure 1 This is a schematic diagram illustrating an implementation scenario of the data processing method provided in this application. The electronic device can be a terminal device, which acquires a first data sequence from a reference measuring device and a second data sequence from a target measuring device. The reference and target measuring devices are located at different positions on the target production line. The first data sequence includes several first data points, and the second data sequence includes several second data points. Based on the target distance between the reference and target measuring devices and the historical production line speed information of the target production line, target data points matching each of the first data points are determined from the second data points. The matched target data points are then aligned with the first data points. Thus, by combining the fluctuating production line speed and the distance between the devices, the data collected by each device is aligned to improve alignment accuracy.

[0028] It should be noted that, Figure 1 The illustrated implementation environment scenario of the data processing method is merely an example. The implementation environment scenario of the data processing method described in this application embodiment is for the purpose of more clearly illustrating the technical solution of this application embodiment and does not constitute a limitation on the technical solution provided in this application embodiment. Those skilled in the art will understand that with the evolution of data processing and the emergence of new business scenarios, the technical solution provided in this application is also applicable to similar technical problems.

[0029] The solutions provided in this application are specifically illustrated through the following embodiments. It should be noted that the order of description of the following embodiments is not intended to limit the preferred order of the embodiments.

[0030] This embodiment will be described from the perspective of a data processing device, which can be integrated into an electronic device, which can be a terminal device and / or a server, and this application does not limit it.

[0031] Please see Figure 2 , Figure 2 This is a schematic flowchart of a data processing method provided in an embodiment of this application. The data processing method is applied to an electronic device and may include the following steps S101 to S103: Step S101: Obtain the first data sequence of the reference measuring device and the second data sequence of the target measuring device, wherein the reference measuring device and the target measuring device are set at different positions on the target production line, the first data sequence includes a number of first data points, and the second data sequence includes a number of second data points.

[0032] The target production line refers to a production line equipped with multiple measuring devices, where the data obtained from the measurements by these devices need to be aligned.

[0033] The measuring equipment is used to measure some parameters of the production line, and its specific parameters can be adjusted according to the actual situation. No restrictions are imposed here.

[0034] For example, the target production line is a roll-to-roll coating production line, which can be used for coating lithium-ion battery electrodes, optical films, and flexible electronic devices. The measuring equipment installed in the target production line includes, but is not limited to, a coating machine, an online X-ray measuring device, and a CCD inspection system. The coating machine is used to control the slurry supply and coating process parameters; the online X-ray measuring device is used to monitor areal density; and the CCD inspection system is used to identify surface defects and measure the dimensional data of the roll material. It is understood that the number of measuring devices of the same type can include one or more. For example, the measuring equipment installed in the target production line includes one coating machine, five online X-ray measuring devices, and one CCD inspection system.

[0035] In this context, the reference measurement device refers to the measurement device used as a benchmark during the data alignment process among multiple measurement devices corresponding to the target production line. The target measurement device refers to the measurement device to be aligned during the data alignment process among multiple measurement devices corresponding to the target production line. The number of target measurement devices can be one or more. Target measurement devices can be measurement devices of a different type than the reference measurement device, or measurement devices of the same type as the reference measurement device but with a different sampling period. The specific type can be adjusted according to the actual situation and is not limited here.

[0036] The first data sequence refers to a data sequence with timing information obtained by measuring the target production line using a reference measuring device. Each element in the first data sequence corresponds to a first data point, which includes a first measurement time. The first measurement time indicates the measurement time corresponding to the first data point output by the reference measuring device. The time difference between the first measurement times corresponding to adjacent first data points in the first data sequence is determined based on the sampling period of the reference measuring device. For example, assuming the reference measuring device is an online X-ray measuring device with a sampling period of 1 second, the following first data sequence can be obtained by measuring the target production line using this reference measuring device: [(10:00:00, 245.6), (10:00:01, 245.7), (10:00:02, 245.0)...]. Here, (10:00:00, 245.6), (10:00:01, 245.7), and (10:00:02, 245.0) each represent a first data point. (10:00:00, 245.6) means that at 10:00:00, the areal density of the target production line measured by the reference measuring equipment is 245.6. Other values ​​are similar and will not be elaborated here.

[0037] The second data sequence refers to a data sequence with timing information obtained by measuring the target production line using the target measuring device. Each element in the second data sequence corresponds to a second data point, which includes a second measurement time. The second measurement time is used to indicate the measurement time corresponding to the second data point output by the target measuring device. The time difference between the second measurement times corresponding to adjacent second data points in the second data sequence is determined based on the sampling period of the target measuring device.

[0038] The methods for obtaining the first and second data sequences can be adjusted according to the actual situation and are not limited here. For example, the first data point measured by the reference measuring device and the first measurement time of the first data point can be acquired in real time, and the first data sequence can be updated based on the first data point and the first measurement time of the first data point. Another example is that the first data point measured by the reference measuring device within a certain time period and the first measurement time of the first data point can be acquired, and the first data sequence can be constructed based on the first data point within that time period and the first measurement time of the first data point. Other methods are similar and will not be elaborated here.

[0039] Step S102: Based on the target distance between the reference measuring device and the target measuring device, and the historical information of the production line speed of the target production line, determine the target data points that match each of the first data points from the second data points.

[0040] Here, the target data point refers to the second data point that matches the first data point. This matching refers to matching at the measurement location on the target production line. The measurement location indicates the product location measured on the target production line. The product location indicates the location of the product produced on the target production line. For example, the product location indicates the location of a specific battery. Specifically, when the first data point measured by the reference measuring equipment is measured at the measurement location on the target production line, and the target measuring equipment measures it, the resulting second data point is the target data point.

[0041] The production line speed history information includes the production line speed at the measurement times contained in the first and second data sequences. Specifically, the production line speed history information includes several production line speeds and the measurement times for each speed. The production line speed indicates the speed of the target production line at a given measurement time. The production line speeds included in the production line speed history information can be fluctuating speeds.

[0042] The target distance indicates the relative distance between the reference measuring device and the target measuring device in the direction of movement of the target production line. In the case of a coil coating production line, the target distance can be understood as the material transfer path distance between the reference measuring device and the target measuring device along the coil conveying path. For example... Figure 3As shown, the measuring devices installed at different positions on the target production line include measuring device A, measuring device B, and measuring device C. Measuring device A and measuring device B are installed at corresponding positions on opposite sides of the target production line, while measuring device A and measuring device C are installed at different positions on the same side of the target production line. Taking measuring device A as the reference measuring device as an example, when the target measuring device is measuring device B, since the relative distance between measuring device A and measuring device B in the direction of movement of the target production line is 0, the target distance between the reference measuring device and the target measuring device in the direction of movement of the target production line is also 0. When the target measuring device is measuring device C, since the relative distance between measuring device A and measuring device C in the direction of movement of the target production line is L1, the target distance between the reference measuring device and the target measuring device in the direction of movement of the target production line is L1.

[0043] It should be noted that the target distance can carry directional information. For example, taking the direction of movement of the target production line as the positive direction, a target distance of +40 meters means that the reference measuring device is located 40 meters upstream of the target measuring device. This means that a product of the target production line first passes the reference measuring device, then moves 40 meters before passing the target measuring device. A target distance of -40 meters means that the reference measuring device is located 40 meters downstream of the target measuring device. This means that a product of the target production line first passes the target measuring device, then moves 40 meters before passing the reference measuring device.

[0044] There are various ways to obtain the target distance, and the specific methods can be adjusted according to the actual situation; no restrictions are placed here. For example, before production begins on the target production line, the position information of each measuring device in the workspace can be measured. The direction of movement of the target production line is taken as the positive direction of the target coordinate axis (such as the X-axis), and a reference point in the workspace is taken as the origin, thus constructing a workspace coordinate system. After determining the reference measuring device, based on the position coordinates of the reference measuring device in the workspace coordinate system, and the position coordinates of other measuring devices (i.e., the target measuring device) in the workspace coordinate system, the distance values ​​between the reference measuring device and the target measuring device on the target coordinate axis are calculated, obtaining the relative distance between the reference measuring device and the target measuring device in the direction of movement of the target production line. Another example is... Figure 4The illustrated process constructs segmented mapping files and distance configuration files for the target production line. These files determine the target distance between the reference measuring device and the target measuring device along the movement direction of the target production line. The segmented mapping file can contain worksheets corresponding to multiple measuring devices. Each worksheet for a different measuring device includes the data point identifiers of its collected data points and the mapping relationship between the measuring device and specific physical segments (also known as physical workstations) on the target production line. Physical segments can be obtained by dividing the target production line according to the measuring devices. Based on the physical distance measured in each physical segment, the distance from each workstation to the reference point (such as a fixed reference point on the reference measuring device) is defined.

[0045] Specifically, the process of determining target data points matching each first data point from the second data points based on the target distance between the reference measuring device and the target measuring device, and the historical production line speed information of the target production line, may include: determining the theoretical measurement time of the target measuring device at the corresponding measurement position of each first data point based on the target distance, the historical production line speed information, and the first measurement time of each first data point; and determining the target data points matching each first data point from the second data points based on the theoretical measurement time and the second measurement time of the second data points.

[0046] The theoretical measurement time refers to the theoretical time at which the target measuring device measures the measurement position corresponding to the first data point.

[0047] There are multiple ways to determine the theoretical measurement time of the target measuring device at each first data point, and the specific method can be adjusted according to the actual situation. No restrictions are imposed here.

[0048] In some embodiments, the process of determining the theoretical measurement time of the target measuring device at the measurement position corresponding to each first data point may include: for each first data point, starting from the first measurement time of the first data point, selecting a target measurement time multiple times according to the unit measurement time; determining the moving distance corresponding to each target measurement time based on the production line speed corresponding to each target measurement time and the unit measurement time; updating the cumulative distance of the target production line moving towards the target measuring device based on the moving distance, until the distance difference between the cumulative distance and the target distance is not greater than a preset distance difference; and using the target measurement time as the theoretical measurement time of the target measuring device at the measurement position corresponding to the first data point.

[0049] The unit measurement time can be adjusted according to the actual situation, and there is no specific restriction here. For example, the unit measurement time can be 1 second, or 1 minute.

[0050] The travel distance indicates the length of the target production line moved per unit measurement time, based on the production line speed at the target measurement time. The cumulative distance indicates the cumulative travel length of the target production line determined based on the production line speed at each target measurement time.

[0051] The preset distance difference is used to determine whether the target production line has moved to the target measuring device. If the difference between the cumulative distance and the target distance is not greater than the preset distance difference, the target production line is determined to have moved to the target measuring device; otherwise, the target production line has not moved to the target measuring device. The size of the preset distance difference can be adjusted according to actual conditions and is not limited here.

[0052] For example, assume the target production line is equipped with a coating machine, five online X-ray measuring devices, and a CCD inspection system. The reference measuring device is the fifth online X-ray measuring device, and the target measuring device is the first online X-ray measuring device. The target distance between the reference and target measuring devices is -40 meters. The theoretical measurement time for the target measuring device to measure the same product position is needed when the reference measuring device measures that position at 10:00:00 on April 25, 2025. The determination process is as follows: Initialize the current time T = 10:00:00 (i.e., the first measurement time), and the cumulative distance S = 0. Starting from T, backtrack by 1 second (i.e., the unit measurement time), selecting the target measurement time multiple times. For example, if the first selected target measurement time is 09:59:59, query the production line speed at that time, and obtain V = 30 m / min at 09:59:59 on April 25, 2025. Based on the production line speed corresponding to the target measurement time and the unit measurement time, the moving distance ΔS corresponding to the target measurement time is determined to be 30 / 60 = 0.5 meters, and the cumulative distance S is updated to 0.5 meters. The second selected target measurement time is 09:59:58, with a corresponding production line speed V = 30.2 m / min, and a corresponding moving distance ΔS ≈ 0.503 meters, and the cumulative distance S is updated to ≈ 1.003 meters. The above steps are repeated. After backtracking for 80 seconds, the cumulative distance S reaches 40 meters. Based on this, it can be determined that 10:00:00 - 80 seconds = 09:58:40 is the theoretical measurement time of the measurement position corresponding to the first data point on the target measurement equipment.

[0053] In this way, dynamic delay backtracking can be performed by combining fluctuating production line speeds to truly reflect material transfer time, thereby improving the data alignment accuracy of different measuring devices.

[0054] Based on this, the above data processing method further includes: if the difference between the cumulative distance and the target distance is greater than a preset distance difference, and if the production line speed corresponding to the target measurement time is abnormal, and the duration of the abnormality is not less than a preset duration threshold, the target production line speed corresponding to the first measurement time of the first data point is obtained; the delay time between the target measuring device and the reference measuring device is determined according to the target distance and the target production line speed; and the theoretical measurement time of the target measuring device at the measurement position corresponding to the first data point is determined based on the delay time and the first measurement time of the first data point.

[0055] The criteria for determining whether the production line speed at the target measurement time is abnormal can be adjusted based on actual circumstances and are not restricted here. For example, an abnormal production line speed at the target measurement time could be a production line speed of 0 or a production line speed lower than the preset production line speed.

[0056] The duration of the anomaly refers to the time interval between multiple consecutive target measurement moments when the production line speed becomes abnormal. For example, assuming a unit measurement moment is 1 second and the number of target measurement moments with abnormal production line speed is 6, the duration of the anomaly is 6 seconds. The preset duration threshold can be adjusted according to actual conditions and is not limited here.

[0057] The delay duration refers to the fixed delay required for the target production line to move between the location of the target measuring equipment and the location of the reference measuring equipment.

[0058] Following the previous example, suppose that when backtracking to the 50th second, the production line speed for the first 10 consecutive seconds is an invalid production line speed (such as the production line speed being empty or <1.0 m / min), then the backtracking is interrupted and switched to the fixed delay algorithm.

[0059] Specifically, please refer to Figure 5 , Figure 5 This is a schematic diagram illustrating the process for determining the theoretical measurement time of the measurement position corresponding to the first data point on the target measurement device, as provided in this application embodiment. The specific determination process includes: obtaining a reference time Td_5 and a target distance D, where the reference time indicates the first measurement time corresponding to a first data point. The current time T = Td_5 and the cumulative distance S = 0 are set to obtain the historical production line speed. If the production line speed is valid, a dynamic delay method is used to determine the theoretical measurement time. If the production line speed is invalid, and the invalidity meets certain conditions (such as the duration of the abnormality not being less than a preset duration threshold), a fixed delay method is used to determine the theoretical measurement time.

[0060] Thus, by providing multiple methods for determining theoretical measurement times, such as dynamic delay and fixed delay, the accuracy of the determined theoretical measurement times can be improved, thereby increasing the accuracy of data alignment based on theoretical measurement times.

[0061] In one embodiment, the process of determining the theoretical measurement time of the target measuring device at the measurement position corresponding to each first data point may include: determining the theoretical measurement time of the target measuring device at the measurement position corresponding to each first data point based on the target distance, the historical information of the production line speed of the target production line, the cumulative displacement information of the encoder, and the first measurement time of each first data point.

[0062] Specifically, when the production line speed is missing in the historical information of the production line speed or the value of the production line speed is continuously lower than the preset speed threshold, the theoretical measurement time is determined based on the cumulative displacement information of the encoder, that is, the time when the cumulative displacement of the encoder reaches the distance of the material conveying path is taken as the theoretical measurement time.

[0063] In some embodiments, the process of determining target data points matching each first data point from the second data points based on the theoretical measurement time and the second measurement time of the second data point may include: for each first data point, determining a time search range for target data points matching the first data points based on the sampling period of the target measurement device and the theoretical measurement time; and determining target data points matching the first data points from the second data points based on the time search range and the second measurement time of the second data points.

[0064] The sampling period refers to the period during which the measuring device samples data points. For example, when the sampling period is 1 second, the time interval between adjacent data points collected by the measuring device is 1 second.

[0065] It should be noted that the sampling periods of different measuring devices can be different or the same. Different measuring devices include two measuring devices of different types, as well as two measuring devices of the same type.

[0066] The time search range refers to the range of measurement times to which the target data point belongs.

[0067] In some embodiments, the process of determining the time search range of the target data point matching the first data point based on the sampling period of the target measuring device and the theoretical measurement time may include: determining the time search range of the target data point matching the first data point based on the sampling period of the target measuring device, the range adjustment coefficient of the sampling period, and the theoretical measurement time.

[0068] Based on this, the process of determining the target data point that matches the first data point from the second data points based on the time search range and the second measurement time of the second data point may include: if there are second data points in the second data sequence whose second measurement time falls within the time search range, determining the target data point that matches the first data point from the second data points whose second measurement time falls within the time search range.

[0069] The method for determining the target data point can be determined according to the actual situation and is not limited here. Preferably, the target data point can be the second data point whose second measurement time falls within the time search range, or the second data point whose second measurement time is closest to the theoretical measurement time. Alternatively, the target data point can be any second data point whose second measurement time falls within the time search range.

[0070] If there is no second data point in the second data sequence whose second measurement time falls within the time search range, the time search range can be further adjusted.

[0071] Specifically, if there is no second data point in the second data sequence whose second measurement time falls within the time search range, the range adjustment coefficient can be updated, and the step of determining the time search range of the target data point that matches the first data point can continue to be performed based on the sampling period of the target measuring device, the range adjustment coefficient of the sampling period, and the theoretical measurement time. The updated range adjustment coefficient is greater than the original range adjustment coefficient.

[0072] For example, two range adjustment coefficients can be configured, such as a first range adjustment coefficient and a second range adjustment coefficient, where the first range adjustment coefficient is smaller than the second range adjustment coefficient. Based on the sampling period of the target measuring device and the first range adjustment coefficient of the sampling period, as well as the theoretical measurement time, a first time search range for the target data point matching the first data point is determined. If there is no second data point in the second data sequence whose second measurement time falls within the first time search range, based on the sampling period of the target measuring device and the second range adjustment coefficient of the sampling period, as well as the theoretical measurement time, a second time search range for the target data point matching the first data point is determined, thus determining whether there is a second data point in the second data sequence whose second measurement time falls within the second time search range. The first time search range is the preferred, smaller time search range. The second time search range is the backup, larger time search range. It is understood that the number of range adjustment coefficients can include two or more, but they must follow the order of determination from smallest to largest time search range.

[0073] In this way, precise matching can be performed first within a smaller time range; if the search fails, the time range restriction can be relaxed, and the search can be tried again within a larger time range to balance matching accuracy and matching success rate.

[0074] In one embodiment, the data processing method further includes: determining a third data point that successfully matches the target data point and a fourth data point that does not match the target data point among the first data points; determining a target time deviation value based on the time deviation value of each third data point, wherein the time deviation value of the third data point is determined based on the second measurement time of the target data point corresponding to the third data point and the theoretical measurement time corresponding to the third data point; updating the theoretical measurement time corresponding to the fourth data point based on the target time deviation value; and continuing to execute the step of determining the time search range of the target data point matching the first data point based on the sampling period of the target measurement device and the theoretical measurement time, based on the updated theoretical measurement time corresponding to the fourth data point.

[0075] The third data point refers to the first data point that successfully matched the target data point. The fourth data point refers to the first data point that did not successfully match the target data point.

[0076] The time deviation value indicates the deviation between the second measurement time of the target data point corresponding to the third data point and the theoretical measurement time corresponding to the third data point. Specifically, the difference between the second measurement time of the target data point corresponding to the third data point and the theoretical measurement time corresponding to the third data point is used as the time deviation value of the third data point.

[0077] The target time deviation value refers to the deviation used to correct the theoretical measurement time of the fourth data point. The target time deviation value is determined based on the time deviation values ​​of each third data point, and its determination method can be adjusted according to actual circumstances; no restrictions are placed here. For example, the target time deviation value can be the average of the time deviation values ​​of each third data point. Another example is that the target time deviation value can be the median of the time deviation values ​​of each third data point.

[0078] There are several ways to update the theoretical measurement time corresponding to the fourth data point based on the target time deviation value. The specific method can be adjusted according to the actual situation and is not limited here. For example, the updated theoretical measurement time corresponding to the fourth data point can be determined based on the sum of the theoretical measurement time corresponding to the fourth data point and the target time deviation value. Another example is to determine the updated theoretical measurement time corresponding to the fourth data point based on the difference between the theoretical measurement time corresponding to the fourth data point and the target time deviation value. Yet another example is to determine the updated theoretical measurement time corresponding to the fourth data point based on the sum of the theoretical measurement time corresponding to the fourth data point and the target time deviation value, along with an adjustment coefficient.

[0079] Based on the updated theoretical measurement time corresponding to the fourth data point, the step of returning the sampling period of the target measurement device and the theoretical measurement time to determine the time search range of the target data point matching the first data point continues to be executed, so as to determine the target data point matching the fourth data point from the second data points that fall within the time search range at the second measurement time.

[0080] It should be noted that if a fourth data point exists in the first data point, it is necessary to determine whether the theoretical measurement time corresponding to the fourth data point needs to be updated. The determination process can be adjusted according to the actual situation, and there are no restrictions here.

[0081] For example, if the third data point meets the preset conditions, a target time deviation value is determined based on the time deviation value of each third data point, so as to update the theoretical measurement time corresponding to the fourth data point according to the target time deviation value.

[0082] The preset conditions can be adjusted according to the actual situation, and there are no restrictions here. For example, the preset conditions include that the number of third data points is greater than a preset number, and / or that the median of the deviation determined based on the time deviation value of each third data point is not greater than the preset median of deviation, and / or that the absolute median difference determined based on the time deviation value of each third data point is not greater than the preset difference.

[0083] The median deviation is the median of the time deviation values. The absolute median difference is the median of the absolute differences between each time deviation value and the median deviation.

[0084] There are several ways to determine the preset median deviation, and the specific method can be adjusted according to the actual situation; no restrictions are imposed here. For example, the preset median deviation may be a preset parameter. Another example is that the preset median deviation may be determined based on the sampling period of the target measuring device and a first coefficient.

[0085] There are several ways to determine the preset difference, and the specific method can be adjusted according to the actual situation; no restrictions are imposed here. For example, the preset difference can be a preset parameter. Another example is that the preset difference is determined based on the sampling period of the target measuring device and the second coefficient.

[0086] In some embodiments, the above data processing method further includes: after matching the fourth data point with a target data point based on the updated theoretical measurement time corresponding to the fourth data point, if there is a fifth data point among the fourth data points that has not been matched with a target data point, then determining multiple reference data points from the second data points; and performing interpolation processing between the reference data points based on the second measurement time corresponding to the reference data point and the theoretical measurement time corresponding to the fifth data point to obtain the target data point corresponding to the fifth data point.

[0087] Among them, the reference data points are the data points determined from the second data points.

[0088] There are various ways of interpolation processing, which can be adjusted according to the actual situation and are not limited here. For example, please refer to Figure 6 , in the second data sequence of the target measurement device, the first valid second data point (i.e., the reference data point) and the last valid second data point (i.e., the reference data point) that have matched the target data point can be determined, and for the first data points that have not been successfully matched within the time range formed by the first valid second data point and the last valid second data point, time interpolation method is used for filling. The time interpolation method refers to linear interpolation based on the time axis, and the steps are as follows: Assume that in the data sequence of a certain measurement device, there are missing points (i.e., the first data points that have not been successfully matched): the first valid data point at the left end is (T1, V1), the first valid data point at the right end is (T2, V2), and the time of the missing point is Tmissing, satisfying T1 < Tmissing < T2. During the process of filling using the time interpolation method, the number of data points required for interpolation can be determined according to the number of the first data points that have not been successfully matched. A maximum continuous interpolation number can also be set to limit the number of data points for interpolation of the first data points that have not been successfully matched during the process of filling using the time interpolation method, so as to limit the interpolation to avoid the situation of end forgery and large-scale false data.

[0089] Step S103: Align the matched target data points and the first data points.

[0090] In this way, by adopting the above data processing method, the first data sequence of the reference measurement device and the second data sequence of the target measurement device can be obtained. Among them, the reference measurement device and the target measurement device are set at different positions on the target production line. The first data sequence includes several first data points, and the second data sequence includes several second data points; according to the target distance between the reference measurement device and the target measurement device, and the historical information of the production line speed of the target production line, the target data points matching each first data point are determined from the second data points; the matched target data points and the first data points are aligned. In this way, by combining the production line speed with fluctuations in the production line and the distances between various devices, the data collected by various devices are aligned to improve the alignment accuracy.

[0091] To better understand the data processing method provided by the embodiments of the present application, an example is used for explanation below. Exemplarily, as Figure 7 shown, the process of the data processing method provided by the embodiments of the present application may include the following steps: Step 10, Configuration Initialization: Read the segmented mapping file and the distance configuration file, and establish the mapping relationship between the measurement points of each data source (i.e., the target measurement device), the physical segments of the production line, and the distance from the production line physical segments to the reference workstation (i.e., the location of the reference measurement device).

[0092] Step 20, Multi-source Data Loading and Preprocessing: Identify and load the data of the reference measurement point (i.e., the reference measurement device) as the reference time series, load the data of equipment such as coater CTP, X-ray, and CCD, and perform time parsing and deduplication.

[0093] Step 30, Dynamic Delay Calculation: Based on the historical production line speed data, calculate the theoretical coating time (i.e., the theoretical measurement time) of each measurement point; if the speed data is missing or abnormal, use the fixed delay algorithm as a fallback.

[0094] Among them, calculating the theoretical coating time of each measurement point may include the speed integration backtracking method, which can be understood as traversing the speed data backward in steps of 1 second, and determining the theoretical coating time when the cumulative distance reaches the configured distance between the target segment and the reference point.

[0095] If the speed is invalid or lower than Vmin for consecutive M seconds, switch to the fixed delay algorithm, where M ranges from 5 to 60, and Vmin ranges from 0.5 to 5 m / min.

[0096] Step 40, Intelligent Tolerance Matching: According to the adaptive sampling period of the data source, adopt a two-level tolerance strategy to match the nearest real data point near the theoretical coating time, and preferentially match non-zero values when the non-zero data ratio exceeds the preset threshold.

[0097] The sampling period Dt is determined by the median of the time index difference, and outliers are trimmed using the P = 95% quantile threshold.

[0098] The two-level tolerance strategy includes: a primary tolerance of 0.6 - 1.0Dt and a secondary tolerance of 1.5 - 2.0Dt, and when the non-zero data ratio in the candidate window exceeds 5 - 20%, non-zero values are preferentially matched. Among them, the tolerance or tolerance range is the time search range mentioned in this application.

[0099] Step 50, System Phase Self-correction: Perform phase correction based on the median of the time deviation of the successfully matched data points, and perform secondary matching on the unmatched points.

[0100] The conditions for phase self-correction include that the number of successful matches N1 of the same measurement point is ≥ 8, and the median absolute deviation MAD < K1 × Dt, and the median deviation |ΔT_median| < K2 × Dt, where K1 ranges from 0.2 to 0.5, and K2 ranges from 0.5 to 1.5.

[0101] Step 60, Mid-segment amplitude limiting interpolation: For data points that are still missing, perform amplitude limiting interpolation within the first and last valid intervals to limit the number of consecutive interpolation points.

[0102] Among them, the mid-segment limiting interpolation is only performed between the first and last valid points, the number of consecutive interpolations L is limited to 2–5, and this interpolation is only applicable to X-ray data.

[0103] Step 70, Output: Generate an alignment data table and a traceability detail table. The detail table records the matching status, filling method, and calculation parameters for each data point.

[0104] Specifically, the traceability details table should include at least: data source, measurement point name, segment location, reference timestamp, theoretical coating time, actual matching time, matching status, filling method, speed, distance, and delay parameters.

[0105] Thus, by using a speed history integration backtracking method instead of fixed-delay calculation, the impact of production line speed fluctuations on material transfer time can be more realistically reflected, effectively reducing alignment deviations. Combined with adaptive tolerance matching and a non-zero priority strategy, the system maintains highly stable matching performance even with data jitter, missing data, or zero-value interference. A residual-based phase self-correction mechanism automatically corrects inherent delays or clock deviations between different devices, avoiding tedious manual calibration. Mid-segment limiting interpolation technology is used, performing limited supplementary points only within the effective data range at the beginning and end, ensuring data continuity and preventing data forgery at the ends or over a large scale. The output traceability details table fully records the source and generation method of each data point (e.g., true matching, phase correction, or interpolation), making the data processing process transparent and traceable, improving the reliability of the results. Configuration-driven operation through segmented mapping files and distance configuration files eliminates the need to modify the core algorithm when the production line layout changes; simply updating the configuration file completes the adaptation, significantly reducing application costs.

[0106] The following description, in conjunction with the accompanying drawings and the following embodiments, illustrates... Figure 7 The data processing flow shown is explained in detail.

[0107] This embodiment takes a lithium battery electrode coating production line as an example. The reference measurement point is selected as the 5th X-ray areal density scanner (D5). The data sources to be traced include X-ray scanners 1-4, CCD measurement data, and process parameter data (CTP).

[0108] First, the segment mapping file is loaded through the configuration module to establish the mapping between the first partition data of the measurement of each measurement point (such as the first partition data of the X-ray surface density scanner and the production line segment (XRAY_1_ScanningFrame_AreaDensity_1 and L1)) and obtain the physical distance from the segment to the D5 frame (such as L1 distance + 10.0 meters).

[0109] The data loading module reads CSV data files from the D5, CTP, CCD, and X-ray (1-4) units, and uniformly parses the timestamps, processing mixed-format time strings. Subsequently, the core alignment module begins the following tasks.

[0110] For example, please refer to Figure 8 For example, consider the timestamp T_d5 of a single D5 data entry: Find the velocity value V near time T_d5 from the 1-second frequency and velocity data after CTP resampling.

[0111] For each measurement point of the X-ray unit 1, its distance from unit D5 is known to be +10 meters. A velocity history integration backtracking method is used: starting from time T_d5, a velocity value V_t is acquired every second in reverse time, the distance moved per second is calculated as V_t / 60 (meters), and these values ​​are accumulated. When the accumulated distance reaches 10 meters, the backtracking stops; this moment is the theoretical time T_theory at which the data for that measurement point was generated.

[0112] In the original data sequence of the measurement point on the first X-ray machine, centered at T, the nearest non-zero value is first searched within the range [T_theory -0.6×Dt, T_theory+0.6×Dt] (where Dt is the sampling period of the data source) (zero-avoidance strategy). If not found, the tolerance range is expanded to [T_theory-1.5×Dt, T_theory+1.5×Dt] for a second search. If a match is found, the value and time deviation (T_match-T_theory) are recorded; if not, it is marked as missing.

[0113] After the initial matching of a whole roll of data is completed, the phase self-correction stage begins: For the measurement point of X-ray frame 1, the median ΔT_median of the time deviation of all successfully matched points is calculated. Then, for all points that failed the initial matching, their theoretical coating time is corrected to T_theory'=T_theory+ΔT_median, and the process returns to the steps of searching for the nearest non-zero value (zero avoidance strategy) within the range of [T_theory-0.6×Dt,T_theory+0.6×Dt] (Dt is the sampling period of the data source) and expanding the tolerance range to [T_theory-1.5×Dt,T_theory+1.5×Dt] for a second search.

[0114] Finally, for points that still cannot be matched, mid-range interpolation is used as a fallback: in the entire data volume of the measurement point, the first and last valid data points are found, and within this interval, the missing points are filled using time interpolation, with a limit on the maximum number of consecutive interpolations (e.g., 5 points).

[0115] The output module ultimately generates two files: aligned_d5_MMDD_XXXX.csv: the aligned data table (covering all CTP / CCD / X-ray data), with each row's timestamp consistent with the D5 data. track_MMDD_XXXX.xlsx: a traceability detail table, containing the theoretical time, matching status (Y / N), and filling method (REAL / REAL_PHASE / INTERP2 / INTERP5) for each data point.

[0116] The above embodiments demonstrate that the method of this application can effectively achieve high-precision and high-robust alignment of multi-source data in the coating production process and provide full-process traceability information.

[0117] Thus, the process involves configuring and initializing production line mapping and distance rules; using the benchmark data time as an anchor point, and calculating the theoretical generation time of other measurement point data through speed history integration; employing a two-level tolerance strategy based on adaptive sampling period for intelligent data matching, prioritizing the matching of non-zero values; performing self-correction and re-matching of unmatched points based on statistical phase offset; performing mid-segment limiting interpolation on still missing data; and finally outputting an aligned data table and a traceability detail table recording the entire matching process. This application solves the problems of low alignment accuracy and poor robustness caused by ignoring speed fluctuations and data anomalies in existing methods, achieving high-precision data alignment with transparent and reliable processes, and providing a reliable data foundation for coating process optimization and quality analysis.

[0118] Based on the above, and addressing the issues in the background technology such as different detection devices being distributed at different positions along the roll material conveying path, independent sampling clocks, and uncertain material transmission time due to speed fluctuations, existing cross-device data alignment methods generally suffer from the following shortcomings: They do not consider dynamic speed changes, and using fixed delays or simple calculations will result in significant positional offset errors under speed fluctuation conditions; different devices have inconsistent sampling periods, lacking a mechanism to adaptively adjust the matching range based on sampling characteristics; cross-device matching lacks robustness, and alignment is prone to failure when zero values, missing points, or intermittent abnormal sampling exist; and they lack traceability, making it impossible to clearly identify the source, matching method, and calculation basis of each alignment point. A multi-source heterogeneous data spatiotemporal alignment method is needed that can comprehensively consider factors such as the transmission path length along the conveying direction between devices, historical speed data or encoder cumulative length, and sampling characteristics of each device, in order to accurately determine the measurement correspondence of different detection devices for the same roll material position.

[0119] This application acquires a first data sequence from a reference measuring device located at different positions along the roll material conveying path and a second data sequence from a target measuring device. It loads configuration information containing the transmission path distance between the two devices along the conveying direction (e.g., the belt travel distance between devices or the cumulative encoder length) to characterize the spatial correspondence of the same roll material position between different devices. Based on the measurement time of the first data point, combined with historical production line speed data or the cumulative conveying distance recorded by the encoder, the material transmission process between the two devices is traced back to calculate the theoretical measurement time of the material position on the target measuring device. When speed fluctuations are large or speed data is abnormal for short periods, a fixed delay estimation based on the current speed is allowed as a backup method. The initial time search range for the theoretical measurement time is automatically determined according to the sampling period of the target measuring device data; if no valid data point is matched initially, a secondary search is performed by increasing the time range; and in the presence of zero values, non-zero valid values ​​are preferentially selected for matching. If a measurement point has a sufficient number of successful matching points in the entire data segment, the deviation distribution between its theoretical measurement time and actual matching time is statistically analyzed. If the deviation meets the set consistency conditions, the median deviation is used as the phase offset, and the unmatched points are time-corrected and matched again. For data points that still do not match after the above steps, a limited interpolation method is used to fill the gap within the interval between the first and last valid data points. The number of consecutive interpolation points does not exceed a set upper limit to avoid over-inference of the ends or abnormal areas. An aligned data table consistent with the time series of the reference measurement equipment is generated based on the matching relationship. At the same time, a tracking detail table is output, recording the theoretical measurement time, actual matching time, matching status, and filling method of each aligned point, realizing process traceability.

[0120] Compared with the prior art, this application has the following beneficial effects: Considering the changes in conveying path distance and speed, dynamic backtracking is performed using historical speed data or encoder cumulative length to make the material correspondence calculation between different equipment data more consistent with the actual conveying process.

[0121] The matching process can adapt to different sampling characteristics and improve the matching success rate under complex working conditions through sampling period estimation and multi-level time window mechanism.

[0122] It has the ability to handle abnormal operating conditions and can maintain alignment continuity through backup delay estimation, phase correction and amplitude limiting interpolation in the case of data loss, zero value interference or speed abnormality.

[0123] It offers full transparency, clearly identifying the source and processing method of each alignment point through a tracking detail table, facilitating subsequent quality analysis and traceability.

[0124] The overall solution is structured and configurable, suitable for different types of continuous roll material production lines, and can be extended to other online measurement equipment scenarios.

[0125] To better implement the above methods, embodiments of this application also provide a data processing apparatus, which can be integrated into an electronic device, such as a terminal or a server. Figure 9 As shown in the illustration, this application also provides a data processing apparatus, which includes: The data acquisition module 201 is used to acquire a first data sequence from a reference measuring device and a second data sequence from a target measuring device, wherein the reference measuring device and the target measuring device are set at different positions on the target production line, the first data sequence includes a number of first data points, and the second data sequence includes a number of second data points. The matching module 202 is used to determine the target data points that match each of the first data points from the second data points based on the target distance between the reference measuring device and the target measuring device, and the historical information of the production line speed of the target production line. Alignment module 203 is used to align the matched target data point with the first data point.

[0126] In some embodiments, the matching module 202 includes: The timing determination submodule is used to determine the theoretical measurement time of the target measuring device at the corresponding measurement position of each first data point based on the target distance, historical production line speed information, and the first measurement time of each first data point. The matching submodule is used to determine the target data points that match each of the first data points from the second data points, based on the theoretical measurement time and the second measurement time of the second data points.

[0127] In some embodiments, the above-mentioned production line speed history information includes several production line speeds and the measurement time of each production line speed.

[0128] Based on this, the aforementioned time-determining sub-modules include: The time selection unit is used to select the target measurement time multiple times for each first data point, starting from the first measurement time of the first data point, according to the unit measurement time. The distance determination unit is used to determine the moving distance corresponding to each target measurement time based on the production line speed corresponding to each target measurement time and the unit measurement time. The update unit is used to update the cumulative distance that the target production line has moved to the target measuring device based on the moving distance, until the distance difference between the cumulative distance and the target distance is not greater than a preset distance difference. The time determination unit is used to take the target measurement time as the theoretical measurement time of the target measuring device at the corresponding measurement position of the first data point.

[0129] In some embodiments, the data processing apparatus further includes: The speed acquisition unit is used to acquire the target production line speed corresponding to the first measurement time of the first data point when the difference between the cumulative distance and the target distance is greater than a preset distance difference, and the production line speed corresponding to the target measurement time is abnormal and the duration of the abnormality is not less than a preset duration threshold. The delay duration determination unit is used to determine the delay duration between the target measuring device and the reference measuring device based on the target distance and the target production line speed. The second time determination unit is used to determine the theoretical measurement time of the target measuring device at the measurement position corresponding to the first data point based on the delay duration and the first measurement time of the first data point.

[0130] In some embodiments, the matching submodule described above includes: The range determination unit is used to determine the time search range of the target data points that match the first data points, based on the sampling period of the target measuring device and the theoretical measurement time, for each first data point; The matching unit is used to determine a target data point that matches the first data point from the second data point based on the time search range and the second measurement time of the second data point.

[0131] In some embodiments, the range determination unit includes: The first range determination subunit is used to determine the time search range of the target data point that matches the first data point based on the sampling period of the target measuring device, the range adjustment coefficient of the sampling period, and the theoretical measurement time. Based on this, the aforementioned matching unit includes: The matching subunit is used to determine the target data point that matches the first data point from the second data points that fall within the time search range at the second measurement time.

[0132] In some embodiments, the data processing apparatus further includes: The update unit is used to update the range adjustment coefficient when there is no second data point in the second data sequence whose second measurement time falls within the time search range; The loop unit is used to continue executing the step of determining the time search range of the target data point that matches the first data point based on the sampling period of the target measuring device, the range adjustment coefficient of the sampling period, and the theoretical measurement time, wherein the updated range adjustment coefficient is greater than the original range adjustment coefficient.

[0133] In some embodiments, the data processing apparatus further includes: The first data point determination unit is used to determine the third data point that successfully matches the target data point and the fourth data point that does not match the target data point among the first data points; The deviation value determination unit is used to determine the target time deviation value based on the time deviation value of each third data point, wherein the time deviation value of the third data point is determined based on the second measurement time of the target data point corresponding to the third data point and the theoretical measurement time corresponding to the third data point. The time update unit is used to update the theoretical measurement time corresponding to the fourth data point based on the target time deviation value; The return unit is used to return the time search range of the target data point that matches the first data point based on the updated theoretical measurement time corresponding to the fourth data point, and then continue the execution of the step.

[0134] In some embodiments, the above-mentioned deviation value determination unit includes: The deviation value determination subunit is used to determine the target time deviation value based on the time deviation value of each third data point, provided that the third data point meets the preset conditions. The preset conditions include at least one of the following: the number of third data points is greater than a preset number; the median deviation determined based on the time deviation value of each third data point is not greater than a preset median deviation; and the absolute median difference determined based on the time deviation value of each third data point is not greater than a preset difference.

[0135] In some embodiments, the data processing apparatus further includes: The second data point determination unit is used to determine multiple reference data points from the second data points after matching the fourth data point with the target data point based on the updated theoretical measurement time corresponding to the fourth data point. If there is a fifth data point in the fourth data point that has not been matched with the target data point, then multiple reference data points are determined from the second data points. The interpolation unit is used to perform interpolation between the reference data points based on the second measurement time corresponding to the reference data point and the theoretical measurement time corresponding to the fifth data point, so as to obtain the target data point corresponding to the fifth data point.

[0136] Thus, using the aforementioned data processing device, the data acquisition module 201 can acquire a first data sequence from the reference measuring device and a second data sequence from the target measuring device. The reference and target measuring devices are positioned at different locations on the target production line. The first data sequence includes several first data points, and the second data sequence includes several second data points. The matching module 202, based on the target distance between the reference and target measuring devices and the historical production line speed information of the target production line, determines target data points from the second data points that match each of the first data points. The alignment module 203 aligns the matched target data points with the first data points. In this way, by combining the fluctuating production line speed and the distance between the devices, the data collected by each device is aligned, thereby improving the alignment accuracy.

[0137] This application also provides an electronic device that integrates any of the data processing devices provided in this application. The electronic device includes: One or more processors; Memory; and One or more applications, wherein the applications are stored in memory and configured to be executed by a processor using the data processing method in any of the embodiments described above.

[0138] This application also provides an electronic device that integrates any of the data processing devices provided in this application. For example... Figure 10 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically: The electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will understand that... Figure 10 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 301 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the memory 302, and by calling data stored in the memory 302, thereby providing overall monitoring of the electronic device. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.

[0139] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and data processing by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as audible and visual prompts, alarm functions, etc.), etc.; the data storage area may store data created based on the use of the electronic device. In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0140] The electronic device also includes a power supply 303 that supplies power to various components. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 303 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0141] The electronic device may also include an input unit 304, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0142] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device loads the executable files corresponding to the processes of one or more applications into the memory 302 according to the following instructions, and the processor 301 runs the applications stored in the memory 302 to realize various functions, as follows: A first data sequence from a reference measuring device and a second data sequence from a target measuring device are acquired, wherein the reference measuring device and the target measuring device are set at different locations on the target production line, the first data sequence includes several first data points, and the second data sequence includes several second data points; Based on the target distance between the reference measuring device and the target measuring device, and the historical information of the production line speed of the target production line, target data points that match each of the first data points are determined from the second data points; Align the target data point with the first data point.

[0143] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0144] Therefore, embodiments of this application provide a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. A computer program is stored thereon, and the computer program is loaded by a processor to execute the steps in any of the data processing methods provided in embodiments of this application. For example, the computer program loaded by the processor can execute the following steps: A first data sequence from a reference measuring device and a second data sequence from a target measuring device are acquired, wherein the reference measuring device and the target measuring device are set at different locations on the target production line, the first data sequence includes several first data points, and the second data sequence includes several second data points; Based on the target distance between the reference measuring device and the target measuring device, and the historical information of the production line speed of the target production line, target data points that match each of the first data points are determined from the second data points; Align the target data point with the first data point.

[0145] This application also provides a computer program product, including a computer program / instructions, which, when executed by a processor, are used to perform steps in any of the data processing methods provided in the application embodiments.

[0146] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the detailed descriptions of other embodiments above, which will not be repeated here.

[0147] In practice, each of the above units or structures can be implemented as an independent entity or can be arbitrarily combined to be implemented as the same or several entities. For the specific implementation of each of the above units or structures, please refer to the previous method embodiments, which will not be repeated here.

[0148] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A data processing method, characterized in that, The method includes: A first data sequence from a reference measuring device and a second data sequence from a target measuring device are acquired, wherein the reference measuring device and the target measuring device are located at different positions on the target production line, the first data sequence includes a plurality of first data points, and the second data sequence includes a plurality of second data points; Based on the target distance between the reference measuring device and the target measuring device, and the historical production line speed information of the target production line, target data points that match each of the first data points are determined from the second data points; The matched target data points are aligned with the first data point.

2. The data processing method according to claim 1, characterized in that, The step of determining target data points matching each of the first data points from the second data points based on the target distance between the reference measuring device and the target measuring device, and the historical production line speed information of the target production line, includes: Based on the target distance, the historical information of the production line speed, and the first measurement time of each of the first data points, the theoretical measurement time of the target measuring device at the corresponding measurement position of each of the first data points is determined; Based on the theoretical measurement time and the second measurement time of the second data point, target data points that match each of the first data points are determined from the second data points.

3. The data processing method according to claim 2, characterized in that, The production line speed history information includes several production line speeds and the measurement time of each production line speed; determining the theoretical measurement time of the target measuring device at the corresponding measurement position of each first data point includes: For each of the first data points, starting from the first measurement time of the first data point, the target measurement time is selected multiple times according to the unit measurement time. Based on the production line speed corresponding to each of the target measurement times and the unit measurement time, the moving distance corresponding to each of the target measurement times is determined; Based on the moving distance, update the cumulative distance the target production line moves toward the target measuring device until the distance difference between the cumulative distance and the target distance is not greater than a preset distance difference. The target measurement time is taken as the theoretical measurement time of the target measurement device at the measurement position corresponding to the first data point.

4. The data processing method according to claim 3, characterized in that, The method further includes: If the distance difference between the cumulative distance and the target distance is greater than the preset distance difference, and if the production line speed corresponding to the target measurement time is abnormal, and the duration of the abnormality is not less than the preset duration threshold, the target production line speed corresponding to the first measurement time of the first data point is obtained. Based on the target distance and the target production line speed, determine the delay time between the target measuring device and the reference measuring device; Based on the delay duration and the first measurement time of the first data point, the theoretical measurement time of the target measuring device at the measurement position corresponding to the first data point is determined.

5. The data processing method according to claim 2, characterized in that, The step of determining target data points matching each of the first data points from the second data points based on the theoretical measurement time and the second measurement time of the second data points includes: For each of the first data points, based on the sampling period of the target measuring device and the theoretical measurement time, the time search range of the target data points that match the first data points is determined; Based on the time search range and the second measurement time of the second data point, a target data point matching the first data point is determined from the second data points.

6. The data processing method according to claim 5, characterized in that, The step of determining the time search range of the target data point matching the first data point based on the sampling period of the target measuring device and the theoretical measurement time includes: Based on the sampling period of the target measuring device and the range adjustment coefficient of the sampling period, as well as the theoretical measurement time, the time search range of the target data point matching the first data point is determined; The step of determining a target data point matching the first data point from the second data points based on the time search range and the second measurement time of the second data point includes: From the second data points that fall within the time search range at the second measurement time, a target data point matching the first data point is determined.

7. The data processing method according to claim 6, characterized in that, The method further includes: If there is no second data point in the second data sequence whose second measurement time falls within the time search range, update the range adjustment coefficient. Continue executing the step of determining the time search range of the target data point matching the first data point based on the sampling period of the target measuring device, the range adjustment coefficient of the sampling period, and the theoretical measurement time, wherein the updated range adjustment coefficient is greater than the original range adjustment coefficient.

8. The data processing method according to claim 5, characterized in that, The method further includes: Identify the third data point that successfully matches the target data point and the fourth data point that does not match the target data point among the first data points; A target time deviation value is determined based on the time deviation value of each of the third data points, wherein the time deviation value of the third data point is determined based on the second measurement time of the target data point corresponding to the third data point and the theoretical measurement time corresponding to the third data point; Based on the target time deviation value, update the theoretical measurement time corresponding to the fourth data point; Based on the updated theoretical measurement time corresponding to the fourth data point, the step of determining the time search range of the target data point matching the first data point based on the sampling period of the target measurement device and the theoretical measurement time continues to be executed.

9. The data processing method according to claim 8, characterized in that, The step of determining the target time deviation value based on the time deviation value of each of the third data points includes: If the third data point meets the preset conditions, the target time deviation value is determined based on the time deviation value of each third data point. The preset conditions include at least one of the following: the number of third data points is greater than a preset number; the median deviation determined based on the time deviation value of each third data point is not greater than a preset median deviation; and the absolute median difference determined based on the time deviation value of each third data point is not greater than a preset difference.

10. The data processing method according to claim 8, characterized in that, The method further includes: Based on the updated theoretical measurement time corresponding to the fourth data point, after matching the fourth data point with target data points, if there is a fifth data point among the fourth data points that has not been matched with a target data point, then multiple reference data points are determined from the second data points. Based on the second measurement time corresponding to the reference data point and the theoretical measurement time corresponding to the fifth data point, interpolation is performed between the reference data points to obtain the target data point corresponding to the fifth data point.

11. An electronic device, characterized in that, It includes a memory and a processor; the memory stores an application program, and the processor is used to run the application program within the memory to perform the steps of the data processing method according to any one of claims 1 to 10.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to implement the steps of the data processing method according to any one of claims 1 to 10.

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