Positioning method and device for poor coating material area, storage medium and equipment

Abstract

The invention discloses a positioning method and device for a poor coating material area, a storage medium and equipment, and belongs to the technical field of lithium ion battery production. The method comprises the following steps: collecting coating surface density data in real time; extracting material area data from the coating surface density data collected in real time, and identifying material area edges according to preset coating surface density process parameters to obtain a material area range; according to a set surface density process parameter threshold value, the order of bad number points, exceeding the normal range, of the edge of the material area is calculated; calculating transverse and longitudinal coordinate positions of bad data points on the coating material by analyzing a bad number point sequence and a data operation encoder point and detection point sequence to obtain two-dimensional coordinate information of a bad area; according to the invention, real-time identification and positioning of poor coating can be realized, and the benefit of improving the quality of the battery cell is obvious and far-reaching.

Description

Technical Field

[0001] This invention relates to a method, apparatus, storage medium, and equipment for locating defective coating areas, belonging to the field of battery cell manufacturing technology. Background Technology

[0002] In the existing coating process, identifying and locating defective areas faces a series of challenges, mainly in the following aspects: First, the commonly used methods for detecting and locating defective areas in battery cell production have many shortcomings. Some traditional manual inspection methods rely on workers' experience and visual judgment, which are inefficient and prone to missed detections and misjudgments. On the other hand, some existing automated inspection and positioning technologies still need improvement in terms of detection accuracy and positioning precision, and cannot meet the increasingly stringent production quality requirements.

[0003] First, the detection accuracy is insufficient, making it difficult to identify minute or hidden defects. Existing technologies (such as traditional visual inspection and contact sensors) are limited by resolution, resulting in insufficient ability to identify coating defects (such as thickness deviations in extremely thin areas, tiny bubbles, and shallow surface scratches). For example, when the coating thickness deviation is less than 5μm, some optical inspection equipment may miss the detection due to low signal-to-noise ratio. For bubbles embedded in the slurry or defects that are similar in color to the substrate, traditional visual algorithms are prone to "false positives" or "false negatives," leading to inaccurate location of defective material areas and allowing defective cells to flow into subsequent processes.

[0004] Secondly, the real-time performance is poor and cannot match the high-speed production rhythm. Coating processes are usually carried out on high-speed production lines (such as coating speeds of tens of meters per minute). Existing technologies, if using offline sampling or low-speed scanning inspection, cannot cover the entire fabric strip in real time. For example, CCD camera inspection systems based on line-by-line scanning may suffer from insufficient frame rate leading to blurred images or data processing delays (such as excessively time-consuming defect analysis algorithms) at high speeds, making it impossible to mark defective areas in time during the material strip movement. This ultimately leads to missed location of defective areas, affecting online sorting efficiency.

[0005] Furthermore, insufficient compatibility makes it difficult to adapt to multi-specification products and complex process scenarios. Existing testing technologies are mostly designed for single product specifications (such as electrodes with fixed width and thickness). When product models change (e.g., different electrode sizes or coating materials for different cell models), testing parameters (such as threshold settings and image recognition templates) need to be readjusted, which is time-consuming and labor-intensive. For example: When the coating material is changed from lithium iron phosphate to ternary material, the slurry color and surface texture are significantly different. Traditional template matching-based algorithms need to be retrained, otherwise the identification of defective material areas will fail due to the failure of feature extraction. For complex processes such as multi-layer coating and irregular-shaped coating (e.g., edge blanking, local thickening), existing technologies lack adaptive adjustment mechanisms, which can easily lead to "over-detection" or "detection blind spots," resulting in positioning deviations.

[0006] In summary, the problems with defect positioning in the existing coating process not only severely restrict the improvement of coating yield, but also increase the probability of battery defects, which urgently need to be solved through technological innovation. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, device, storage medium and equipment for locating areas with poor coating. By real-time detection of coating surface density and configuration of relevant process parameters, real-time identification and location of coating defects can be achieved, which brings significant and far-reaching benefits to improving the quality of battery cells.

[0008] To achieve the above objectives / to solve the above technical problems, the present invention is implemented using the following technical solution: First aspect: A method for locating areas of defective coating material, the method comprising: Real-time acquisition of coating surface density data; Extract material zone data from the real-time collected coating surface density data, and identify the edge of the material zone and obtain the range of the material zone according to the preset coating surface density process parameters; Based on the set areal density process parameter threshold, calculate the order of defective points at the edge of the material zone that exceed the normal range; By analyzing the sequence of defective data points and the sequence of encoder and detection points, the horizontal and vertical coordinate positions of the defective data points on the coating material are calculated, thus obtaining the two-dimensional coordinate information of the defective area.

[0009] Optionally, the coating surface density data is collected in real time by directly connecting to a testing instrument, and a locally stored CSV log file is used as a backup data source.

[0010] The above technical solution allows for the use of backup CSV data as a data source when the equipment is offline or the testing instrument malfunctions, achieving the same production testing effect.

[0011] Optionally, the coating surface density process parameters include: The process parameters include the threshold value W for material zone transition point, the material zone advance value P, the maximum allowable value max for surface density, the minimum allowable value min for surface density, the number of material zones n, and the total number of material widths m. These process parameters are configured independently for single-sided coating and double-sided coating, and the parameter field structures are the same.

[0012] The above technical solution: Since single-sided coating is completed during double-sided coating, the areal density of double-sided coating includes single-sided coating and needs to be flexibly set according to different processes.

[0013] Optionally, the detection data is an array data[Q] of Q data points collected each time, where each data point corresponds to a surface density detection value with a width of 0.5-1mm, and Q is the number of data points collected.

[0014] The above technical solution requires that the number of data collected be determined based on the actual reading or foil width, and the width corresponding to a single data point also needs to be set according to the actual detection accuracy.

[0015] Optionally, identifying the edge of the material area and obtaining the range of the material area includes: Iterate through the detection data array data[Q] and find the data interval where the continuous value is greater than the threshold W of the material zone transition point; For each continuous interval, take the leftmost data point that meets the condition and move it P data points to the right as the left edge Lx of the material zone, and take the rightmost data point that meets the condition and move it P data points to the left as the right edge Rx of the material zone.

[0016] The above technical solution allows for setting appropriate W and P values ​​for different products and production processes, making this material area edge algorithm suitable for a variety of products.

[0017] Optionally, the order of the number of defective points whose edges exceed the normal range in the calculation of the material area includes: Extract the data for each material area from data[Q]: left material area Ld1, Ld2, Ld3…Lda, right material area Rd1, Rd2, Rd3…Rda. Divide the material area array into n intervals and obtain the array for each interval: Ld1_1, Ld1_2, Ld1_3…Ld1_n; Ld2_1, Ld2_2, Ld2_3…Ld2_n;…;Rda_1, Rda_2, Rda_3…Rda_n; Calculate the mean Dm for each interval, compare it with the threshold of standard surface density. When surface density max ≥ Dm > surface density min, it is considered normal; otherwise, it is considered defective. From this, obtain the defective point index value Lda_n or Rda_n to get the order of defective points. Q is the number of data points collected.

[0018] The above technical solution: different groupings in the material area can achieve different detection accuracies and effects. The higher the number of groups, the higher the detection and recognition accuracy. However, an excessively high number of groups may also lead to an increase in system performance and false judgment rate, and adjustments need to be made according to actual production.

[0019] Optionally, the calculation of the transverse and longitudinal coordinate positions of the defective data points on the coating material includes: When the detection direction D is positive: The vertical coordinate Y is calculated using the formula: Y = P*△m - (T / Q) × (Q - Lda_n) × V; The horizontal coordinate is Lda_n, in mm; The coordinates of the defective point are output as (Y, Lda_n); When the detection direction D is reversed: The vertical coordinate Y is calculated using the formula: Y = P*△m - (T / Q) × Rda_n × V; The horizontal coordinate is Rda_n, in mm; The coordinates of the defective point are output as (Y, Rda_n); Where P is the current value of the PLC encoder, △m is the distance the encoder moves per unit value in meters, T is the detection cycle in seconds, V is the running speed in meters per second, Rda_n and Lda_n are the position indices of the defective data points in the detection width direction, and Q is the number of data points collected.

[0020] The above technical solution provides a highly adaptable, high-precision, and automated defect coordinate calculation method that can effectively support bidirectional detection systems, achieve stable and reliable defect location and recording, and provide an accurate data foundation for process improvement and quality traceability.

[0021] Second aspect: A positioning device for areas with defective coating, the device comprising: The data acquisition module is configured to collect coating surface density data in real time; The parameter configuration module is configured to extract the coating surface density data collected in real time, identify the edge of the material area based on preset process parameters, and obtain the range of the material area. The material area identification module is configured to perform segmented analysis on the identified material area data based on the set areal density process threshold, and to determine whether there are any defective data intervals that exceed the normal range. The coordinate calculation module is configured to combine the data to run the encoder point and detection point sequence and detection direction information, calculate the horizontal and vertical coordinate positions of the defective data points on the coating material, and obtain the two-dimensional coordinate information of the defective area.

[0022] Third aspect: A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in the first aspect.

[0023] Fourth aspect: An electronic device including a processor and a memory, the memory storing an executable program, wherein the processor executes the program to implement the method of the first aspect.

[0024] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: 1. Identification and Judgment of Defective Material Areas: By collecting coating surface density data and combining it with preset process parameter thresholds and algorithms, this patent can quickly and accurately identify various coating defect features. Whether it is a slight surface density deviation, a local density change, or defects such as bubbles and delamination hidden inside the slurry, the system can automatically extract abnormal data features and compare and analyze them with the standard process model in real time to efficiently determine the existence and type of defective material areas. Compared with traditional manual sampling or single threshold detection methods, this technology greatly improves the timeliness and accuracy of defect identification, effectively avoids missed detections and false detections, intercepts early quality risks at the production front end, and significantly reduces the risk of defective products flowing into subsequent processes. 2. Location of defective material areas: By combining abnormal areal density data with the equipment's operating trajectory, not only can the lateral location of the defect be determined, but the longitudinal location can also be calculated using timestamps and traction belt speed. This achieves millimeter-level precision in locating defective areas, further narrowing the positioning range and providing accurate guidance for rapid sorting, targeted repair, or partial removal of defective products. This significantly reduces production losses caused by ambiguous positioning of defective material areas and effectively improves the yield and quality stability of battery cell production. Attached Figure Description

[0025] Figure 1 The figure shown is a schematic cross-sectional view of the device in operation according to one embodiment of the present invention; Figure 2 The image shown is a top view of the coating process in one embodiment of the present invention; Figure 3 The diagram shown is a schematic diagram of the material storage area in one embodiment of the present invention; Figure 4 The image shown is a single-sided data waveform diagram in one embodiment of the present invention; Figure 5 The image shown is a double-sided data waveform diagram in one embodiment of the present invention; Figure 6 The diagram shown is a flowchart of one embodiment of the method for locating defective coating areas according to the present invention. Figure 7 The diagram shown is a flowchart of the sequential calculation of defective points according to the present invention. Detailed Implementation

[0026] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0027] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are used only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0028] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0029] Example 1 discloses a method for locating areas of defective coating material, the method comprising: Step 1: Collect coating surface density data in real time; Step 2: Extract material area data from the real-time collected coating surface density data, and identify the edge of the material area and obtain the range of the material area according to the preset coating surface density process parameters; Step 3: Calculate the order of defective points at the edge of the material zone that exceed the normal range based on the set areal density process parameter threshold; Step 4: By analyzing the sequence of defective data points and the sequence of data execution encoder points and detection points, calculate the horizontal and vertical coordinate positions of the defective data points on the coating material to obtain the two-dimensional coordinate information of the defective area.

[0030] In this embodiment, the process settings configuration parameters are as follows: material zone transition point threshold: W; material zone advance value: P; maximum material zone value: max; minimum material zone value: min; number of material zone intervals: n; number of material widths: m; the parameters distinguish between single-sided and double-sided, but the fields are the same. The coating surface density data is collected in real time through direct connection to the testing instrument, and a locally stored CSV log file is used as a backup data source. In case of communication failure or data loss, the log data can be parsed immediately to provide a reliable backup for the data and effectively ensure the integrity of the data.

[0031] Data acquisition, detection data: data

[2000] , 2000 data points are collected each time, and each data point represents a detection width of 1mm.

[0032] In the specific implementation process of this embodiment, the identification of the material area edge includes: traversing the detection data array data

[2000] and searching for data intervals with continuous values ​​greater than the threshold W of the material area transition point; For each continuous interval, take the leftmost data point that meets the condition and move it P data points to the right as the left edge Lx of the material zone, and take the rightmost data point that meets the condition and move it P data points to the left as the right edge Rx of the material zone. This gives us the edges of the material zones: {L1,R1},{L2,R2},{L3,R3}…{La,Ra}; Mx = |Rx - Lx| / 2. This allows us to obtain the left material zone {L1,M1},{L2,M2},{L3,M3}…{La,Ma} and the right material zone {M1,R1},{M2,R2},{M3,R3}…{Ma,Ra} for each material zone. Figure 3 As shown.

[0033] like Figure 7As shown, in the specific implementation process of this embodiment, the data of each material area in data

[2000] is extracted, the left material area Ld1, Ld2, Ld3…Lda, and the right material area Rd1, Rd2, Rd3…Rda. The material area array is divided into n intervals, and the array of each interval is obtained as follows: Ld1_1, Ld1_2, Ld1_3…Ld1_n; Ld2_1, Ld2_2, Ld2_3…Ld2_n;…; Rda_1, Rda_2, Rda_3…Rda_n; the mean value of each interval is calculated as Dm; the standard threshold is compared. When max≥Dm>min, it indicates normal, and others indicate defective. Thus, the defective point index value Lda_n or Rda_n is obtained. Specifically, by sorting the defective data points of the defective material area and the data running encoder points and detection point order, the horizontal and vertical coordinate values ​​of the defective points are obtained. Extract the data from each material area in data

[2000] : left material area Ld1, Ld2, Ld3…Lda, right material area Rd1, Rd2, Rd3…Rda. Divide the material area array into n intervals and obtain the array for each interval: Ld1_1, Ld1_2, Ld1_3…Ld1_n; Ld2_1, Ld2_2, Ld2_3…Ld2_n;…; Rda_1, Rda_2, Rda_3…Rda_n; Calculate the mean of each interval: Dm; Compare with the standard threshold. When max≥Dm>min, it indicates normal; otherwise, it indicates defective. From this, obtain the defective point index value Lda_n or Rda_n; Figure 7 As shown, assume that X data points are collected in a single scan, the number of material zones is n, and each material zone contains X / n data points; there are x1, x2, x3, ..., xn arrays; take the mean of each array, extract all arrays whose mean is greater than the threshold W of the material zone transition point, and obtain the edge of the material zone by adding (or subtracting) the material zone advancement value P according to the array index (+, - according to the positive and negative directions and the left and right edges).

[0034] In the specific implementation of this embodiment, the method for calculating the two-dimensional coordinate information of the defective area includes: When the detection direction D is positive: The vertical coordinate Y is calculated using the formula: Y = P - (T / 2000) × (2000 - Lda_n) × V The horizontal coordinate is Lda_n (unit: mm). The coordinates of the defective point are output as (Y, Lda_n); When the detection direction D is reversed: The vertical coordinate Y is calculated using the formula: Y = P - (T / 2000) × Rda_n × V The horizontal coordinate is Rda_n (unit: mm). The coordinates of the defective point are output as (Y, Rda_n); Where P is the current value of the PLC encoder, T is the detection cycle in seconds, V is the running speed in m / s, and Rda_n and Lda_n are the position indices of the defective data points in the detection width direction.

[0035] Specifically, when detection data is received, the detection direction D, detection cycle T, PLC encoder value P, running speed V, and unit pulse value P_Dat are obtained; through the following calculations: Positive: Coating meters ; Since the width range of each inspection is 2000mm, each data point represents 1mm. From this, the inspection position Lda_n mm can be obtained, and the coordinates (Y, Lda_n) can be obtained. Reverse: Coating meters: ; Since the width range of each inspection is 2000mm, each data point represents 1mm. Thus, the inspection position Rda_n mm can be obtained, and the coordinates (Y, Rda_n) can be obtained.

[0036] Example 2: A positioning device for areas with defective coating, the device comprising: The data acquisition module is configured to collect coating surface density data in real time; The parameter configuration module is configured to extract the coating surface density data collected in real time, identify the edge of the material area based on preset process parameters, and obtain the range of the material area. The material area identification module is configured to perform segmented analysis on the identified material area data based on the set areal density process threshold, and to determine whether there are any defective data intervals that exceed the normal range. The coordinate calculation module is configured to combine the data to run the encoder point and detection point sequence and detection direction information, calculate the horizontal and vertical coordinate positions of the defective data points on the coating material, and obtain the two-dimensional coordinate information of the defective area.

[0037] Example 3: A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in Example 1.

[0038] Example 4: An electronic device includes a processor and a memory, wherein the memory stores an executable program, and the processor executes the program to implement the method described in Example 1.

[0039] In summary, the above embodiments have the following technical effects: Areal density data acquisition: This patent employs a direct connection to the testing instrument to read the test data in real time, ensuring the timeliness and accuracy of the data. Simultaneously, a local parsing log CSV file serves as a backup data source. In the event of communication failures or data loss, the log data can be immediately parsed, providing a reliable backup and effectively guaranteeing data integrity.

[0040] Material area identification: By setting process threshold parameters, a specific algorithm is used to accurately obtain the edge of the material area, thereby quickly and accurately identifying the coating material area. This can efficiently obtain the range of the material area and provide accurate regional information for subsequent calculations.

[0041] Defect Identification: Based on pre-set process parameters and real-time detection data, a specific algorithm is used to analyze and process the data, quickly locating defective data points. This helps to promptly identify quality problems in the production process and provides strong support for quality control. By acquiring coating areal density data at millisecond-level high frequency and combining it with preset process parameter thresholds and algorithms, this patent can quickly and accurately identify various coating defect characteristics. Whether it is a slight areal density deviation, a local density change, or defects such as bubbles and delamination hidden inside the slurry, the system can automatically extract abnormal data features and perform real-time comparison and analysis with the standard process model to efficiently determine the existence and type of defective areas. Compared with traditional manual sampling or single threshold detection methods, this technology significantly improves the timeliness and accuracy of defect identification, effectively avoiding missed or false detections, intercepting early quality risks at the production front end, and significantly reducing the risk of defective products flowing into subsequent processes.

[0042] Defect Location: By analyzing the sequence of defective data points in the defective material area, as well as the sequence of encoder and detection points, the system can accurately calculate the lateral and longitudinal coordinates of the defective points, achieving precise location of the defect. This facilitates quick problem identification and resolution by staff, improving production efficiency and product quality. Based on the dynamic correlation between real-time areal density data and equipment operating parameters, this patent can accurately locate the defective material area to its specific physical location. By establishing a spatial coordinate mapping model, the system combines areal density anomaly data with the equipment's operating trajectory. This not only determines the lateral location of the defect (such as a specific area within the coating width) but also calculates the longitudinal location using timestamps and traction belt speed, achieving millimeter-level precision in defect area location. Simultaneously, the system supports multi-dimensional cross-validation, correlating changes in upstream and downstream process parameters with equipment operating status to further narrow the location range. This provides precise guidance for rapid sorting, targeted repair, or partial rejection of defective products, significantly reducing production losses caused by ambiguous defective material area location and effectively improving the yield and quality stability of battery cell production.

[0043] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for locating areas of defective coating material, characterized in that, The method includes: Real-time acquisition of coating surface density data; Extract material zone data from the real-time collected coating surface density data, and identify the edge of the material zone and obtain the range of the material zone according to the preset coating surface density process parameters; Based on the set areal density process parameter threshold, calculate the order of defective points at the edge of the material zone that exceed the normal range; By analyzing the sequence of defective data points and the sequence of encoder and detection points, the horizontal and vertical coordinate positions of the defective data points on the coating material are calculated, thus obtaining the two-dimensional coordinate information of the defective area.

2. The method for locating defective coating areas according to claim 1, characterized in that, The coating surface density data is collected in real time by directly connecting to the testing instrument, and a locally stored CSV log file is used as a backup data source.

3. The method for locating defective coating areas according to claim 1, characterized in that, The coating surface density process parameters include: The process parameters include the threshold value W for material zone transition point, the material zone advance value P, the maximum allowable value max for surface density, the minimum allowable value min for surface density, the number of material zones n, and the total number of material widths m. These process parameters are configured independently for single-sided coating and double-sided coating, and the parameter field structures are the same.

4. The method for locating defective coating areas according to claim 1, characterized in that, The detection data is an array data[Q] that collects Q data points each time. Each data point corresponds to a surface density detection value with a width of 0.5-1mm, and Q is the number of data points collected.

5. The method for locating defective coating areas according to claim 4, characterized in that, The process of identifying the edge of the material area and obtaining the range of the material area includes: Iterate through the detection data array data[Q] and find the data interval where the continuous value is greater than the threshold W of the material zone transition point; For each continuous interval, take the leftmost data point that meets the condition and move it P data points to the right as the left edge Lx of the material zone, and take the rightmost data point that meets the condition and move it P data points to the left as the right edge Rx of the material zone.

6. The method for locating defective coating areas according to claim 1, characterized in that, The order of defective points at the edge of the calculated material area that exceed the normal range includes: Extract the data for each material area from data[Q]: left material area Ld1, Ld2, Ld3…Lda, right material area Rd1, Rd2, Rd3…Rda. Divide the material area array into n intervals and obtain the array for each interval: Ld1_1, Ld1_2, Ld1_3…Ld1_n; Ld2_1, Ld2_2, Ld2_3…Ld2_n;…;Rda_1, Rda_2, Rda_3…Rda_n; Calculate the mean Dm for each interval, compare it with the threshold of standard surface density. When surface density max ≥ Dm > surface density min, it is considered normal; otherwise, it is considered defective. From this, obtain the defective point index value Lda_n or Rda_n to get the order of defective points. Q is the number of data points collected.

7. The method for locating defective coating areas according to claim 1, characterized in that, The calculation of the defective data points' lateral and longitudinal coordinate positions on the coating material includes: When the detection direction D is positive: The vertical coordinate Y is calculated using the formula: Y = P*△m - (T / Q) × (Q - Lda_n) × V; The horizontal coordinate is Lda_n, in mm; The coordinates of the defective point are output as (Y, Lda_n); When the detection direction D is reversed: The vertical coordinate Y is calculated using the formula: Y = P*△m - (T / Q) × Rda_n × V; The horizontal coordinate is Rda_n, in mm; The coordinates of the defective point are output as (Y, Rda_n); Where P is the current value of the PLC encoder, △m is the distance the encoder moves per unit value in meters, T is the detection cycle in seconds, V is the running speed in meters per second, Rda_n and Lda_n are the position indices of the defective data points in the detection width direction, and Q is the number of data points collected.

8. A positioning device for a defective coating area, characterized in that, The device includes: The data acquisition module is configured to collect coating surface density data in real time; The material area identification module is configured to extract material area data from the real-time collected coating surface density data, and identify the edge of the material area and obtain the range of the material area according to the preset coating surface density process parameters. The calculation module is configured to calculate the order of defective points at the edge of the material zone that are outside the normal range, based on a set threshold for the areal density process parameter. The coordinate calculation module is configured to calculate the lateral and longitudinal coordinate positions of defective data points on the coating material by analyzing the order of defective data points and the order of encoder points and detection points, thereby obtaining the two-dimensional coordinate information of the defective area.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 7.

10. An electronic device comprising a processor and a memory, characterized in that, The memory stores an executable program, and when the processor executes the program, it implements the method as described in any one of claims 1 to 7.

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