Negative pressure channel blockage detection method based on voltage dispersion and flatness

Abstract

The invention relates to the technical field of battery formation, and discloses a negative pressure channel blockage detection method based on voltage dispersion and planeness, which comprises the following steps: acquiring a plurality of end voltages of each negative pressure channel, and analyzing the voltage dispersion based on each end voltage to obtain a dispersion analysis result; determining abnormal negative pressure channels with abnormities according to the dispersion analysis result, and determining the risk level of each abnormal negative pressure channel according to an abnormal risk assessment standard; and sequentially measuring the flatness of the battery corresponding to each abnormal negative pressure channel based on the risk level of the abnormal negative pressure channel, and detecting whether the negative pressure channel corresponding to the abnormal battery is blocked or not according to the flatness of the corresponding battery. According to the invention, by identifying the voltage data dispersion and comparing the battery flatness data, the abnormal storage location / negative pressure channel is traced, the suspicious storage location is provided for engineering personnel, the completely blocked channel can be accurately positioned, and early warning can be provided for the abnormal condition of blocking in the process or slow channel blocking.

Description

Technical Field

[0001] This invention relates to the field of battery formation technology, and specifically to a method for detecting blockages in negative pressure channels based on voltage dispersion and flatness. Background Technology

[0002] Amidst the global wave of accelerated energy restructuring towards clean and low-carbon energy, the new energy industry is booming at an unprecedented pace. As one of the core supporting technologies for this transformation, lithium-ion batteries, with their superior energy density, cycle life, and continuously decreasing costs, have become a key carrier in modern energy storage systems. They are widely used in diverse fields such as electric vehicles, portable electronic devices, grid-connected renewable energy storage, and backup power, profoundly changing how we acquire and utilize energy. Formation is one of the key processes in lithium-ion battery production, directly affecting the battery's initial efficiency, cycle life, safety, and consistency. This process involves the initial charge and discharge of the battery after electrolyte injection, activating the electrode materials and forming a stable solid electrolyte interphase (SEI) film. The stable, precise, and reliable operation of the formation equipment is crucial for ensuring battery quality. Therefore, developing efficient and accurate anomaly identification technology for formation equipment has a strong practical need and driving force for innovation.

[0003] Currently, negative pressure formation is widely used in the industry, but the monitoring and anomaly identification of formation equipment have some shortcomings. There is an over-reliance on threshold alarms and human experience. Alarms are triggered by setting fixed thresholds for key parameters such as battery voltage, current, temperature, and air pressure. This method is insensitive to slow degradation and faults (channel blockage) where parameters do not exceed thresholds but the mode is abnormal. Currently, periodic negative pressure fixtures are used to detect channel blockage, but this still relies on comparative threshold setting methods, which are insufficient for detecting blockages during the process or slow channel blockage, and lack early warning capabilities for abnormal modes. Battery voltage is affected by equipment failure, inherent differences, and environmental interference. When troubleshooting equipment problems, engineers need to enter the equipment or use fixtures for verification, which is time-consuming and labor-intensive, and cannot accurately pinpoint the location of the faulty battery or channel. Summary of the Invention

[0004] In view of this, the present invention provides a negative pressure channel blockage detection method based on voltage dispersion and flatness to solve the problems of lack of early warning for blocked channels and inability to accurately locate faulty channels.

[0005] In a first aspect, the present invention provides a method for detecting blockage in negative pressure channels based on voltage dispersion and flatness, wherein each negative pressure channel corresponds to a battery for formation experiments, and the ending voltage of the last step of each formation experiment is recorded. The method includes: Multiple termination voltages for each negative voltage channel are obtained, and the voltage dispersion is analyzed based on each termination voltage to obtain the dispersion analysis results. Based on the dispersion analysis results, abnormal negative pressure channels with anomalies were identified, and the risk level of each abnormal negative pressure channel was determined according to the abnormal risk assessment standard. Based on the risk level of the abnormal negative pressure channel, the flatness of the battery corresponding to each abnormal negative pressure channel is measured sequentially, and the abnormal battery is identified according to the flatness of the corresponding battery. The blockage of the negative pressure channel corresponding to the abnormal battery is then detected.

[0006] The negative pressure channel blockage detection method based on voltage dispersion and flatness provided by this invention traces abnormal storage locations / negative pressure channels by comparing voltage data dispersion and battery flatness data. This provides engineers with suspected storage locations, reduces downtime for entering equipment or using tooling for calibration, and overcomes the shortcomings of the original rolling inspection method, which could not detect abnormal storage locations in a timely manner. It can accurately locate completely blocked channels and provide early warnings for abnormal situations such as blockage during the process or slow blockage of channels, which is beneficial for controlling battery quality.

[0007] In one optional implementation, the voltage dispersion is analyzed based on each termination voltage to obtain the dispersion analysis results, including: Plot a scatter plot of each termination voltage, and calculate the overall average termination voltage and overall standard deviation of each termination voltage based on the scatter plot; The termination voltage threshold is calculated based on the overall average termination voltage and the overall standard deviation. The termination voltage thresholds are plotted on a scatter plot, and each termination voltage is compared with the termination voltage threshold to obtain the dispersion analysis results.

[0008] In one optional implementation, identifying abnormal negative pressure channels based on dispersion analysis results includes: Based on the results of the dispersion analysis, abnormal termination voltages that are greater than the termination voltage threshold are determined. The negative voltage channel with an abnormal termination voltage is designated as the abnormal negative voltage channel.

[0009] The negative pressure channel blockage detection method based on voltage dispersion and flatness provided by this invention determines the voltage threshold by plotting the termination voltage scatter plot, calculating the overall average termination voltage and standard deviation, and comparing it with each termination voltage. This allows for intuitive and accurate identification of abnormal situations where the termination voltage exceeds the threshold, thereby pinpointing the abnormal negative pressure channel. It does not rely on complex equipment or manual experience; simple statistical analysis can efficiently troubleshoot channel blockage problems, enabling early warning and preventing the escalation of faults. At the same time, it can quickly locate the abnormal position, reduce downtime for troubleshooting, and ensure the stable operation of the formation equipment.

[0010] In one optional implementation, the voltage dispersion is analyzed based on each termination voltage to obtain the dispersion analysis results, and the method further includes: Calculate the mean and standard deviation of the termination voltage for each termination voltage at the target storage location, and calculate the standard score for each termination voltage based on the mean and standard deviation of the termination voltage. A scatter plot of the standard scores is drawn based on the standard scores of each end voltage, and the dispersion of each end voltage is analyzed based on the standard score threshold to obtain the dispersion analysis results of the target storage location.

[0011] In one optional implementation, identifying abnormal negative pressure channels based on dispersion analysis results includes: Based on the dispersion analysis results of the target storage location, the abnormal termination voltage when the standard score is greater than the standard score threshold is determined; The negative voltage channel with an abnormal termination voltage is designated as the abnormal negative voltage channel.

[0012] The present invention provides a negative pressure channel blockage detection method based on voltage dispersion and flatness. By calculating the mean, standard deviation, and standard score of the termination voltage, drawing a scatter plot of the standard score and combining it with threshold analysis, the voltage dispersion can be accurately quantified, avoiding misjudgments caused by absolute voltage differences, improving the scientific nature and accuracy of anomaly identification, efficiently screening out abnormal termination voltages with standard scores exceeding the threshold, and thus accurately locating abnormal negative pressure channels. This enables early and accurate blockage warnings, reduces reliance on manual experience, improves troubleshooting efficiency, and ensures the stable operation of the formation equipment.

[0013] In one optional implementation, the risk level of each abnormal negative pressure channel is determined according to an abnormal risk assessment standard, including: Count the number of abnormal termination voltages in each negative voltage channel and the total number of abnormalities in all negative voltage channels; Calculate the abnormality rate of each negative pressure channel relative to the total number of abnormalities based on the number of abnormalities in each negative pressure channel. The risk level of each negative pressure channel is determined based on the abnormal ratio and the range of abnormal risk ratios.

[0014] The negative pressure channel blockage detection method based on voltage dispersion and flatness provided by this invention determines the risk level by statistically analyzing the number and proportion of abnormal termination voltages in each negative pressure channel and combining this with the abnormal risk ratio range. This enables hierarchical management of negative pressure channel blockage risk, intuitively distinguishing between high, medium, and low risk channels, allowing investigation resources to be precisely allocated to high-risk channels, improving the priority and efficiency of anomaly handling; avoiding resource waste caused by indiscriminate investigation; and identifying potential high-risk channels in advance to prevent fault expansion and ensure stable operation of the formation process.

[0015] In one optional implementation, the flatness of the battery corresponding to each abnormal negative pressure channel is measured sequentially based on the risk level of the abnormal negative pressure channel, and the abnormal battery is identified according to the flatness of the corresponding battery. The process of detecting whether the negative pressure channel corresponding to the abnormal battery is blocked includes: Batteries corresponding to abnormal negative pressure channels are selected as the batteries to be measured in descending order of risk level. Identify the plane with the largest area of ​​the battery to be measured, and mark the test points on the plane; The height of each test point was measured using a height gauge, and the height of the point with the largest absolute value was selected as the height of the most convex point by comparison. Compare the height of the most convex point with a preset height threshold, filter out abnormal batteries whose height of the most convex point is greater than the preset height threshold, and check whether the negative pressure channel corresponding to the abnormal battery is blocked.

[0016] The negative pressure channel blockage detection method provided by this invention, based on voltage dispersion and flatness, measures the flatness of the corresponding battery in descending order of risk level, prioritizing high-risk channels to improve the targeting and efficiency of blockage investigation. By selecting the largest flat surface of the battery and marking the test point, and using a height gauge to measure the height of the most convex point, combined with a preset threshold to screen abnormal batteries, it can accurately verify the channel blockage situation and avoid misjudgment caused by other interference factors due to voltage abnormalities. This reduces unnecessary full-scale testing and ensures the accuracy of blocked channel identification through physical characteristic verification, helping to quickly locate the fault point.

[0017] Secondly, this invention provides a negative pressure channel blockage detection device based on voltage dispersion and flatness, wherein each negative pressure channel corresponds to a battery for formation experiments, and the device records the end voltage of the last step of each formation experiment. The device includes: The voltage dispersion analysis module is used to obtain multiple end voltages for each negative voltage channel, and analyze the voltage dispersion based on each end voltage to obtain the dispersion analysis results. The abnormal negative pressure channel identification module is used to identify abnormal negative pressure channels based on the dispersion analysis results, and to determine the risk level of each abnormal negative pressure channel according to the abnormal risk assessment standard. The flatness measurement and blockage channel determination module is used to sequentially measure the flatness of the battery corresponding to each abnormal negative pressure channel based on the risk level of the abnormal negative pressure channel, determine the abnormal battery based on the flatness of the corresponding battery, and detect whether the negative pressure channel corresponding to the abnormal battery is blocked.

[0018] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method described in the first aspect or any corresponding embodiment thereof.

[0019] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the method described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a schematic flowchart of a negative pressure channel blockage detection method based on voltage dispersion and flatness according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating another negative pressure channel blockage detection method based on voltage dispersion and flatness according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the end voltage scatter plot in the negative pressure channel blockage detection method based on voltage dispersion and flatness according to an embodiment of the present invention; Figure 4 This is a schematic diagram of selecting test points using a nine-square grid in the negative pressure channel blockage detection method based on voltage dispersion and flatness according to an embodiment of the present invention; Figure 5 This is a schematic diagram of a physical object used for measurement in a negative pressure channel blockage detection method based on voltage dispersion and flatness according to an embodiment of the present invention; Figure 6 This is a flowchart illustrating another method for detecting blockage in a negative pressure channel based on voltage dispersion and flatness according to an embodiment of the present invention. Figure 7 This is a schematic diagram of the scatter plot of the Z value corresponding to the end voltage in the negative pressure channel blockage detection method based on voltage dispersion and flatness according to an embodiment of the present invention; Figure 8 This is a structural block diagram of a negative pressure channel blockage detection device based on voltage dispersion and flatness according to an embodiment of the present invention; Figure 9 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] The booming development of the new energy battery industry and the increasingly stringent requirements for battery performance, safety, and cost have placed higher standards on the stability and intelligence of core manufacturing equipment—the formation equipment. In production activities, the 5M1E (a general term referring to the six main factors causing product quality fluctuations) is commonly used for analysis. When no abnormalities are found in personnel, materials, methods, environment, and measurement checks, the equipment check utilizes the method proposed in this application to detect whether the formation storage area is blocked by data on battery voltage dispersion and battery flatness. This improves the level of anomaly identification in the battery manufacturing process and ensures product quality with lower investment costs.

[0024] Existing solutions mostly focus on monitoring single or a few parameters. However, the voltage of a lithium-ion battery is one of its core parameters. Current solutions do not effectively integrate battery voltage into the operating status of the equipment itself. Voltage data during the formation stage is affected by three factors: ① Equipment malfunctions leading to abnormal voltage: Poor venting during formation negative pressure causes voltage increases; ② Individual battery differences: In the industry, this is called poor self-discharge, manifested as a larger voltage drop rate during the battery's resting phase, which is not significant during formation; ③ Environmental interference: The final voltage decreases when the formation environment temperature rises. Because the formation process takes place in the same environment, the impact on battery voltage is consistent. Therefore, under the same formation process without abnormal alarms, equipment malfunctions can lead to abnormal voltage. Conventional detection of blockages requires equipment engineers to enter the equipment or use tooling for verification, which is time-consuming and labor-intensive, and still cannot accurately pinpoint the location / channel of the abnormality.

[0025] To address the aforementioned issues, this invention provides a method for detecting blockages in negative pressure channels based on voltage dispersion and flatness. By comparing voltage data dispersion and battery flatness data, this method aims to provide early warning of blocked channels and accurately locate faulty channels.

[0026] According to an embodiment of the present invention, a method for detecting blockage of negative pressure channels based on voltage dispersion and flatness is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] This embodiment provides a negative pressure channel blockage detection method based on voltage dispersion and flatness, which can be used in the aforementioned computer system. Figure 1 This is a flowchart of a negative pressure channel blockage detection method based on voltage dispersion and flatness according to an embodiment of the present invention, as shown below. Figure 1 As shown, the process includes the following steps: Step S101: Obtain multiple end voltages for each negative voltage channel, and analyze the voltage dispersion based on each end voltage to obtain the dispersion analysis results.

[0028] Specifically, in a normal battery, sufficient contact between the electrolyte and the electrodes can effectively conduct ions and reduce polarization. However, when the channel is blocked and venting is not smooth, gas retention will reduce ion conduction efficiency. But when the charging current is continuously input, the overall cell voltage will show a "false increase" due to the weakened polarization, that is, the voltage rises faster, but the actual capacity does not increase. Therefore, identifying the corresponding storage location / channel of the voltage discrete battery and conducting targeted investigations can help improve the anomaly identification rate.

[0029] In the formation workshop, a fixed storage location specifically used to store batteries undergoing the formation process is called a "formation storage location". The formation workshop has environmental control, and the formation storage locations are placed vertically. The naming rule is: row-column-layer. For example, "storage location: 1-2-2, aisle: 18" means that the formation storage location is the first row, second column, and second layer.

[0030] An independent charging and discharging circuit (with independently adjustable parameters such as voltage, current, and temperature) used to control the formation process of a battery is called a "formation channel". In this embodiment, we take a storage location with 24 channels (arranged in 2×12) as an example. The naming rule is to determine the position in sequence. For example, "storage location: 1-2-2, channel: 18" represents the position of the battery with serial number 18 in the second layer of the first row and second column.

[0031] Each negative pressure channel corresponds to one battery for formation experiments. Formation generally uses a small current step charging method, and the final voltage V of the last step in each formation experiment is recorded. 结束 (Unit: V / volt) As a key point of analysis, the termination voltage includes data from formation of multiple batches of batteries and data from multiple formations of the same battery. Based on multiple termination voltages, a dispersion analysis is performed on each negative voltage channel to identify termination voltages that significantly deviate from the normal range, which are then used as the results of the dispersion analysis.

[0032] Step S102: Based on the dispersion analysis results, identify the abnormal negative pressure channels that are abnormal, and determine the risk level of each abnormal negative pressure channel according to the abnormal risk assessment standard.

[0033] Specifically, based on the dispersion analysis results, the abnormal negative pressure channels corresponding to batteries with abnormal termination voltages are identified. By counting the number of abnormal voltage occurrences for each abnormal negative pressure channel, the risk level of each abnormal negative pressure channel can be determined, and they can be divided into high, medium, and low risk levels so that high-risk negative pressure channels can be prioritized for subsequent processing.

[0034] Step S103: Based on the risk level of the abnormal negative pressure channel, measure the flatness of the battery corresponding to each abnormal negative pressure channel in sequence, determine the abnormal battery according to the flatness of the corresponding battery, and detect whether the negative pressure channel corresponding to the abnormal battery is blocked.

[0035] Specifically, since there are various reasons for an increase in the termination voltage, including malfunctions in other equipment such as voltage test probes, abnormal batteries identified through dispersion analysis need to be further examined by assessing the flatness of the produced batteries to determine if the corresponding negative pressure channel is blocked. Existing flatness analysis methods can be used to analyze the battery flatness. If the battery surface is relatively flat without obvious protrusions or depressions, it indicates good flatness and is a normal battery; otherwise, it indicates an abnormal battery.

[0036] The negative pressure channel blockage detection method based on voltage dispersion and flatness provided in this embodiment traces abnormal storage locations / negative pressure channels by comparing voltage data dispersion and battery flatness data. This provides engineers with suspected storage locations, reduces downtime for entering the equipment or using tooling for calibration, and overcomes the shortcomings of the original rolling inspection method, which could not detect abnormal storage locations in a timely manner. It can accurately locate completely blocked channels and provide early warnings for abnormal situations such as blockage during the process or slow blockage of channels, which is beneficial for controlling battery quality.

[0037] This embodiment provides a negative pressure channel blockage detection method based on voltage dispersion and flatness, which can be used in the aforementioned computer system. Figure 2 This is a flowchart of a negative pressure channel blockage detection method based on voltage dispersion and flatness according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps: Step S201: Obtain multiple end voltages for each negative voltage channel, and analyze the voltage dispersion based on each end voltage to obtain the dispersion analysis results.

[0038] Specifically, step S201 includes: Step S2011: Draw a scatter plot of each termination voltage, and calculate the overall average termination voltage and overall standard deviation of each termination voltage based on the scatter plot.

[0039] Specifically, in scenarios requiring rapid preliminary screening of a large number of battery voltage anomalies, such as when a suspected anomaly occurs on a production line but the suspicious storage location needs to be quickly located, a scatter plot can be drawn by collecting the end voltage data of all batteries under the same environment and process. A threshold can be quickly set using "average voltage + 3.5 × standard deviation" to visually filter out batteries with voltage exceeding the standard. Then, the number of anomalies in the corresponding storage location can be counted to determine the priority. This allows for preliminary anomaly location to be completed in a short time, saving time for subsequent in-depth analysis.

[0040] Collect process data from normal production activities under the same environment and process, and use this data as an example to aid understanding. Plot a scatter plot of the final voltage using Excel or other software, such as... Figure 3 As shown, when the distribution range of the scatter plot is stable, the scatter plot screening method can be used. First, the overall average termination voltage is calculated based on all termination voltage points in the scatter plot. and overall standard deviation The calculation process is a mature existing technology and will not be described in detail here.

[0041] Step S2012: Calculate the termination voltage threshold based on the overall average termination voltage and the overall standard deviation.

[0042] Specifically, based on the overall average termination voltage and the overall standard deviation, the termination voltage threshold is calculated using the following formula:

[0043] in, Indicates the end voltage threshold. Indicates the overall average ending voltage. This represents a constant (which can be determined experimentally; a larger value indicates more discrete points being selected). This represents the overall standard deviation. For example... Figure 3 As shown, the overall average termination voltage is The overall standard deviation is Based on experience and needs, this was determined. ,but Figure 3 The end voltage threshold in the middle can be taken as This is just an example, but it is not the only one.

[0044] It should be noted that the choice of the value of n directly affects the accuracy and false positive rate of outlier detection. Based on statistical theory and probability density function quantification, in a standard normal distribution (μ=0, σ=1): P(|Z| ≤ 3.5) = 99.96% P(|Z|>3.5) = 0.04% This means that out of every 10,000 data points, only 4 points are expected to naturally fall within this interval, which is an extremely low probability event. The method described in this patent is intended to identify extreme outliers, and ±3.5σ covers 99.96%, which satisfies the screening of extreme outliers. It facilitates accurate identification of outliers with a low false positive rate. In actual use, there is no universally applicable optimal threshold, only the threshold most suitable for the current scenario, as shown in Table 1 below.

[0045] Table 1. Classic Threshold Standards (Normal Distribution Scenarios)

[0046] Step S2013: Plot the termination voltage threshold onto a scatter plot and compare each termination voltage with the termination voltage threshold to obtain the dispersion analysis results.

[0047] Specifically, the termination voltage threshold is plotted on a scatter plot, such as... Figure 3 As shown, the red line represents the termination voltage threshold. By comparing each termination voltage with the termination voltage threshold, the points above the red line represent termination voltages that are greater than the termination voltage threshold.

[0048] Step S202: Based on the dispersion analysis results, identify the abnormal negative pressure channels that are abnormal, and determine the risk level of each abnormal negative pressure channel according to the abnormal risk assessment standard.

[0049] Specifically, in step S202 above, determining the abnormal negative pressure channels based on the dispersion analysis results includes: Step S2021: Based on the dispersion analysis results, determine the abnormal termination voltages where the termination voltage is greater than the termination voltage threshold.

[0050] Specifically, based on the dispersion analysis results, abnormal termination voltages that exceed the termination voltage threshold are identified, such as... Figure 3 As shown, the points above the red line represent termination voltages that exceed the termination voltage threshold, which are abnormal termination voltages.

[0051] Step S2022: The negative voltage channel with abnormal termination voltage is designated as the abnormal negative voltage channel.

[0052] Specifically, the storage location information and negative pressure channel information corresponding to the abnormal ending voltage are statistically analyzed. The negative pressure channel with abnormal ending voltage is designated as the abnormal negative pressure channel. As shown in Table 2, this is a statistical table of storage locations and channels corresponding to abnormal ending voltage.

[0053] Table 2. Statistics of Abnormal End Voltage Storage Locations and Channels

[0054] In this embodiment, the higher the abnormal ending voltage count, the higher the risk, and the more prioritized for investigation. This results in faster identification and quicker output of suspicious storage locations for detection. The risk level can be determined based on the proportion of abnormal ending voltage counts for a specific storage location or channel to the total number of abnormal ending voltages. For example, a proportion > 10% is considered high risk, 10% ≤ proportion < 5% is considered medium risk, and proportion ≤ 5% is considered low risk. This is merely an example and not a limitation.

[0055] The negative pressure channel blockage detection method based on voltage dispersion and flatness provided in this embodiment determines the voltage threshold by plotting the termination voltage scatter plot, calculating the overall average termination voltage and standard deviation, and comparing it with each termination voltage. This allows for intuitive and accurate identification of abnormal situations where the termination voltage exceeds the threshold, thereby pinpointing the abnormal negative pressure channel. It does not rely on complex equipment or manual experience; simple statistical analysis can efficiently troubleshoot channel blockage problems, achieve early warning, and prevent the fault from escalating. At the same time, it can quickly locate the abnormal position, reduce downtime for troubleshooting, and ensure the stable operation of the formation equipment.

[0056] In step S202 above, the risk level of each abnormal negative pressure channel is determined according to the abnormal risk assessment standard, including: Step S2023: Count the number of abnormal termination voltages in each negative voltage channel and the total number of abnormal voltages in all negative voltage channels.

[0057] Specifically, for each negative pressure channel, the number of times its abnormal termination voltage occurs (i.e., the number of times the battery termination voltage corresponding to that channel exceeds the normal range) is recorded one by one. At the same time, the abnormal number of all negative pressure channels is summarized to obtain the total number of abnormal numbers of all negative pressure channels.

[0058] Step S2024: Calculate the abnormality ratio of each negative pressure channel to the total number of abnormalities based on the number of abnormalities in each negative pressure channel.

[0059] Specifically, based on the number of abnormal occurrences in each negative pressure channel and the total number of abnormal occurrences, the percentage of abnormal occurrences in each negative pressure channel is calculated using the formula: "Abnormality percentage of a channel = Number of abnormal occurrences in a channel ÷ Total number of abnormal occurrences × 100%". For example, if a channel has 13 abnormal occurrences and the total number of abnormal occurrences is 47, its abnormality percentage is approximately 13 ÷ 47 × 100% ≈ 27.66%. This percentage is a key quantitative indicator for measuring the risk level of that channel.

[0060] Step S2025: Determine the risk level of each negative pressure channel based on the abnormal ratio and the range of abnormal risk ratios.

[0061] Specifically, by combining a preset range of abnormal risk ratios (e.g., >10% is high risk, 10% ≤ <4% is medium risk, and ≤4% is low risk), the abnormal ratio of each channel is compared with the abnormal risk ratio range to determine the risk level of each abnormal negative pressure channel. For example, a channel with an abnormal ratio of 27.66% will be identified as high risk and prioritized for handling during subsequent investigations, thus achieving hierarchical management of abnormal channels and improving the targeting and efficiency of fault handling.

[0062] The negative pressure channel blockage detection method based on voltage dispersion and flatness provided in this embodiment determines the risk level by statistically analyzing the number and proportion of abnormal termination voltages in each negative pressure channel and combining this with the abnormal risk ratio range. This enables hierarchical management of negative pressure channel blockage risk, intuitively distinguishing between high, medium, and low risk channels, allowing investigation resources to be precisely allocated to high-risk channels, improving the priority and efficiency of anomaly handling, avoiding resource waste caused by indiscriminate investigation, and identifying potential high-risk channels in advance to prevent fault expansion and ensure stable operation of the formation process.

[0063] Step S203: Based on the risk level of the abnormal negative pressure channel, measure the flatness of the battery corresponding to each abnormal negative pressure channel in sequence, determine the abnormal battery according to the flatness of the corresponding battery, and detect whether the negative pressure channel corresponding to the abnormal battery is blocked.

[0064] Specifically, step S203 includes: Step S2031: Sequentially obtain the batteries corresponding to the abnormal negative pressure channels as the batteries to be measured in order of risk level from high to low.

[0065] Specifically, batteries corresponding to abnormal negative pressure channels are taken out from the storage location in descending order of risk level for measurement. For multiple abnormal negative pressure channels with the same risk level, they can be measured in descending order of the percentage of abnormal termination voltage to ensure that high-risk battery channels are processed first.

[0066] Step S2032: Determine the plane with the largest area of ​​the battery to be measured, and select test points on the plane for marking.

[0067] Specifically, flatness belongs to form tolerance, which refers to the maximum deviation of the actual surface from the ideal plane. It indicates the flatness of the large surface. The measurement area should avoid non-functional surfaces such as welds and marking areas (usually the center 80% area of ​​the large surface is taken).

[0068] This embodiment uses the nine-square grid flatness test method to measure the center position of the large surface of the battery (the two opposing planes with the largest area of ​​the square aluminum-cased battery). Figure 4 The test points are marked as shown, where 1-9 represent selected test points.

[0069] Step S2033: Measure the height of each test point using a height gauge, and select the maximum height as the height of the most convex point by comparison.

[0070] Specifically, any point on the plane marked with test points can be selected as the reference point. In this embodiment, a point near the lower right corner of the battery is selected as the reference point. The height gauge is calibrated to zero, and then the battery is moved to test points 1-9 respectively, and the height gauge values ​​are recorded. Figure 5 The diagram illustrates a flatness test using a height gauge. After measurement, the heights of each test point are compared, and the point with the highest height is selected as the most convex point, with its corresponding height taken as the height of the most convex point. In this embodiment, selecting nine test points can reduce errors; the more test points, the smaller the error. This embodiment uses nine test points as an example, but it is not a limitation.

[0071] Step S2034: Compare the height of the most convex point with a preset height threshold, filter out abnormal batteries whose height of the most convex point is greater than the preset height threshold, and detect whether there is an abnormality in the negative pressure channel corresponding to the abnormal battery.

[0072] Specifically, for batteries formed under negative pressure, the height of the most convex point is ≤0 under normal conditions without blockage. The flatness of batteries in each abnormal negative pressure channel is confirmed. The average value of the most convex point of normally produced batteries is about -0.05mm (the average value of the verified batteries in this batch). Therefore, the preset height threshold can be set to 0 or -0.05mm according to the actual situation.

[0073] The flatness information of the battery corresponding to each abnormal negative pressure channel is statistically analyzed, as shown in Table 3. The larger the height of the highest convex point, the higher the risk of the corresponding channel. The processing priority is determined according to the size of the height of the highest convex point of the battery corresponding to each channel. Channels with high and medium risk levels are investigated first. For channels with a high proportion of abnormal termination voltage (i.e., high and medium risk) and a height of the highest convex point > -0.05mm, the channel blockage is confirmed.

[0074] Table 3. Statistics on the flatness of batteries with a frequency > 2

[0075] Low-risk items, due to their low frequency, can be temporarily left unconfirmed and marked for priority confirmation during subsequent rolling investigations.

[0076] The negative pressure channel blockage detection method based on voltage dispersion and flatness provided in this embodiment measures the flatness of the corresponding battery in descending order of risk level, prioritizing high-risk channels to improve the targeting and efficiency of blockage investigation. By selecting the largest flat surface of the battery and marking the test point, and using a height gauge to measure the height of the most convex point, combined with a preset threshold to screen abnormal batteries, it can accurately verify the channel blockage situation and avoid misjudgment caused by other interference factors due to voltage abnormalities. This reduces unnecessary full-scale testing and ensures the accuracy of blockage channel identification through physical characteristic verification, helping to quickly locate the fault point.

[0077] This embodiment provides a negative pressure channel blockage detection method based on voltage dispersion and flatness, which can be used in the aforementioned computer system. Figure 6 This is a flowchart of a negative pressure channel blockage detection method based on voltage dispersion and flatness according to an embodiment of the present invention, as shown below. Figure 6 As shown, the process includes the following steps: Step S301: Obtain multiple end voltages for each negative voltage channel, and analyze the voltage dispersion based on each end voltage to obtain the dispersion analysis results.

[0078] Specifically, step S301 includes: Step S3011: Calculate the mean and standard deviation of the end voltage for each end voltage at the target storage location, and calculate the standard score for each end voltage based on the mean and standard deviation of the end voltage.

[0079] Specifically, for scenarios requiring high accuracy in anomaly identification and quantification of anomaly severity, the Z-score method is employed. For instance, in large-scale lithium-ion battery formation production lines, it is necessary to perform detailed voltage dispersion analysis on the 24 channels of batteries in each storage location. By combining the Z-score with the percentage of anomalies, the risk levels of different storage locations can be accurately distinguished, providing a reliable basis for subsequent targeted investigations and avoiding missed or incorrect assessments.

[0080] The Z-score is applicable when the data follows a normal distribution. Therefore, data preprocessing is required to obtain the end voltage of all negative pressure channels in the same storage location and calculate the mean of the end voltage of all negative pressure channels in the same storage location. and standard deviation The standard fraction (Z value) of the terminal voltage of each battery is calculated using the following formula:

[0081] in, Represents standard scores. This indicates any ending voltage in that storage location. This indicates the average ending voltage of the storage location. This indicates the standard deviation of the ending voltage of the storage location.

[0082] Step S3012: Draw a scatter plot of the standard scores based on the standard scores of each storage location, and analyze the dispersion of each end voltage based on the standard score threshold to obtain the dispersion analysis results of the target storage location.

[0083] Specifically, the Z-values ​​of all batteries in the storage locations are summarized and plotted as follows: Figure 7 The scatter plot of the standard scores shown has the terminal voltage on the x-axis and the Z-value corresponding to the terminal voltage on the y-axis. The standard score threshold is a constant and can be determined according to actual needs. A larger value indicates more discrete points being filtered. In this embodiment, the standard score threshold is 3.5. Figure 7 The red line is used to denote points above the outlier.

[0084] It should be noted that the selection of the standard score threshold (Z threshold) is the same as the selection rule of the n value in step S2012 of the previous embodiment, see Table 1, and will not be repeated here.

[0085] Step S302: Based on the dispersion analysis results, identify the abnormal negative pressure channels that are abnormal, and determine the risk level of each abnormal negative pressure channel according to the abnormal risk assessment standard.

[0086] Specifically, in step S302 above, determining the abnormal negative pressure channels based on the dispersion analysis results includes: Step S3021: Based on the dispersion analysis results of the target storage location, determine the abnormal termination voltage where the standard score is greater than the standard score threshold.

[0087] Specifically, such as Figure 7 As shown in Table 4, taking a standard score threshold of 3.5 as an example, the storage location and channel information corresponding to the end voltage with a standard score greater than 3.5 are statistically analyzed.

[0088] Table 4. Statistics of Storage Locations with Z-values ​​> 3.5

[0089] Similarly, the more times a message is sent, the higher the count, and the higher the priority for investigation. A percentage greater than 10% is considered high risk, 10% ≤ percentage < 4% is considered medium risk, and percentage ≤ 4% is considered low risk. This solution involves data processing and has a slightly slower response time, but it can more accurately identify abnormal channels within the same storage location specification, such as "Storage Location: 2-10-4, Channel: 11", which can be identified using this solution.

[0090] Step S3022: The negative voltage channel with abnormal termination voltage is designated as the abnormal negative voltage channel.

[0091] Specifically, the storage location information and negative pressure channel information corresponding to the abnormal ending voltage are statistically analyzed, and the negative pressure channel with abnormal ending voltage is identified as the abnormal negative pressure channel.

[0092] The negative pressure channel blockage detection method based on voltage dispersion and flatness provided in this embodiment calculates the mean, standard deviation, and standard score of the termination voltage, plots the standard score scatter plot, and combines it with threshold analysis. This method can accurately quantify the degree of voltage dispersion, avoid misjudgment caused by absolute voltage differences, improve the scientificity and accuracy of anomaly identification, efficiently screen out abnormal termination voltages with standard scores exceeding the threshold, and thus accurately locate abnormal negative pressure channels. This enables early and accurate blockage warnings, reduces reliance on manual experience, improves troubleshooting efficiency, and ensures the stable operation of the formation equipment.

[0093] Step S303: Based on the risk level of each abnormal negative pressure channel, sequentially measure the flatness of the battery corresponding to each abnormal negative pressure channel, and determine the blocked negative pressure channel based on the flatness of the corresponding battery. For details, please refer to... Figure 2 Step S203 of the illustrated embodiment will not be described again here.

[0094] In one specific embodiment, the negative pressure channel blockage detection method based on voltage dispersion and flatness described in this application is used to analyze the formation end voltage and battery flatness to identify abnormal storage locations and negative pressure channels. High-priority storage locations are confirmed, and all four high-frequency storage location channels show significant blockage. For medium-priority storage locations, some are found to have bent negative pressure pipes with varying degrees of bend, indicating poor venting. In summary, the blockage of formation negative pressure pipes can be detected by combining voltage dispersion and battery flatness data. The detection rate for complete blockage of negative pressure channels is significantly higher than that of normal rolling inspections.

[0095] Through actual verification, based on the scatter plot screening channel statistics table shown in Table 5, and after confirmation using flatness data, the high-risk and medium-risk blocked channels were finally removed for confirmation. The high-risk channel identification rate was 100%, and the medium-risk channel identification rate was 75%. It can be concluded that the channel meeting the following criteria can be used in the future: a proportion > 10% (high-risk) + the most prominent point of the produced cell > 0.4mm (value obtained from the verification scheme, which varies for different battery specifications) to detect whether the formation negative pressure pipeline is blocked.

[0096] Table 5 Statistical Table

[0097] This embodiment also provides a negative pressure channel blockage detection device based on voltage dispersion and flatness. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0098] This embodiment provides a negative pressure channel blockage detection device based on voltage dispersion and flatness. Each negative pressure channel corresponds to a battery for formation experiments, and the ending voltage of the last step in each formation experiment is recorded. Figure 8 As shown, it includes: The voltage dispersion analysis module 801 is used to acquire multiple end voltages of each negative voltage channel and analyze the voltage dispersion based on each end voltage to obtain the dispersion analysis results.

[0099] The abnormal negative pressure channel determination module 802 is used to determine the abnormal negative pressure channels with abnormalities based on the dispersion analysis results, and to determine the risk level of each abnormal negative pressure channel according to the abnormal risk assessment standard.

[0100] The flatness measurement and blockage channel determination module 803 is used to measure the flatness of the battery corresponding to each abnormal negative pressure channel in sequence based on the risk level of the abnormal negative pressure channel, determine the abnormal battery according to the flatness of the corresponding battery, and detect whether the negative pressure channel corresponding to the abnormal battery is blocked.

[0101] In some alternative implementations, the voltage dispersion analysis module 801 includes: The termination voltage calculation unit is used to draw scatter plots of each termination voltage and calculate the overall average termination voltage and overall standard deviation of each termination voltage based on the scatter plots.

[0102] The termination voltage threshold determination unit is used to calculate the termination voltage threshold based on the overall average termination voltage and the overall standard deviation.

[0103] The first comparative analysis unit is used to plot the termination voltage threshold onto a scatter plot and compare each termination voltage with the termination voltage threshold to obtain the dispersion analysis results.

[0104] In some optional implementations, the abnormal negative pressure channel determination module 802 includes: The first abnormal termination voltage determination unit determines the abnormal termination voltage when the termination voltage is greater than the termination voltage threshold based on the dispersion analysis results.

[0105] The first abnormal negative voltage channel determination unit is used to identify negative voltage channels with abnormal termination voltages as abnormal negative voltage channels.

[0106] The abnormality count unit is used to count the number of abnormal termination voltages in each negative pressure channel and the total number of abnormalities in all negative pressure channels.

[0107] The abnormality percentage calculation unit is used to calculate the abnormality percentage of each negative pressure channel relative to the total number of abnormalities, based on the number of abnormalities in each negative pressure channel.

[0108] The risk level determination unit is used to determine the risk level of each negative pressure channel based on the abnormality ratio and the range of abnormal risk ratios.

[0109] In some alternative implementations, the voltage dispersion analysis module 801 further includes: The standard score calculation unit is used to calculate the mean and standard deviation of the end voltage for each end voltage at the target storage location, and to calculate the standard score for each end voltage based on the mean and standard deviation of the end voltage.

[0110] The second comparative analysis unit is used to draw a scatter plot of the standard scores based on the standard scores of each end voltage, and to analyze the dispersion of each end voltage based on the standard score threshold, so as to obtain the dispersion analysis results of the target storage location.

[0111] In some optional implementations, the abnormal negative pressure channel determination module 802 further includes: The second abnormal termination voltage determination unit is used to determine the abnormal termination voltage when the standard score is greater than the standard score threshold based on the dispersion analysis results of the target storage location.

[0112] The second abnormal negative voltage channel determination unit is used to identify negative voltage channels with abnormal termination voltages as abnormal negative voltage channels.

[0113] In some alternative implementations, the flatness measurement and blocked channel determination module 803 includes: The battery screening unit is used to sequentially select batteries corresponding to the abnormal negative pressure channel as batteries to be measured in order of risk level from high to low.

[0114] The test point marking unit is used to determine the plane with the largest area of ​​the battery to be measured, and to select test points on the plane for marking.

[0115] The unit for determining the height of the most convex point is used to measure the height of each test point using a height gauge, and selects the one with the largest absolute value as the height of the most convex point by comparison.

[0116] The blockage detection unit is used to compare the height of the most convex point with a preset height threshold, filter out abnormal batteries whose height of the most convex point is greater than the preset height threshold, and detect whether the negative pressure channel corresponding to the abnormal battery is blocked.

[0117] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0118] In this embodiment, the negative pressure channel blockage detection device based on voltage dispersion and flatness is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0119] This invention also provides a computer device having the above-described features. Figure 8 The device shown is a negative pressure channel blockage detection device based on voltage dispersion and flatness.

[0120] Please see Figure 9 , Figure 9 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 9 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 9 Take a processor 10 as an example.

[0121] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GPA), or any combination thereof.

[0122] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0123] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0124] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0125] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0126] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0127] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for detecting blockage in negative pressure channels based on voltage dispersion and flatness, wherein each negative pressure channel corresponds to a battery for formation experiments, and the ending voltage of the last step in each formation experiment is recorded, characterized in that... The method includes: Multiple termination voltages for each negative voltage channel are obtained, and the voltage dispersion is analyzed based on each termination voltage to obtain the dispersion analysis results. Based on the dispersion analysis results, abnormal negative pressure channels with anomalies are identified, and the risk level of each abnormal negative pressure channel is determined according to the abnormal risk assessment standard. Based on the risk level of the abnormal negative pressure channel, the flatness of the battery corresponding to each abnormal negative pressure channel is measured sequentially, and the abnormal battery is determined according to the flatness of the corresponding battery. The blockage of the negative pressure channel corresponding to the abnormal battery is then detected.

2. The method according to claim 1, characterized in that, The voltage dispersion was analyzed based on each termination voltage, and the dispersion analysis results were obtained, including: Plot a scatter plot of each termination voltage, and calculate the overall average termination voltage and overall standard deviation of each termination voltage based on the scatter plot; The termination voltage threshold is calculated based on the overall average termination voltage and the overall standard deviation. The termination voltage threshold is plotted on the scatter plot, and each termination voltage is compared with the termination voltage threshold to obtain the dispersion analysis results.

3. The method according to claim 2, characterized in that, Based on the dispersion analysis results, abnormal negative pressure channels were identified, including: Based on the dispersion analysis results, abnormal termination voltages that are greater than the termination voltage threshold are determined. The negative voltage channel with an abnormal termination voltage is designated as the abnormal negative voltage channel.

4. The method according to claim 1, characterized in that, The voltage dispersion was analyzed based on each termination voltage, and the dispersion analysis results were obtained, including: Calculate the mean and standard deviation of the termination voltage for each termination voltage at the target storage location, and calculate the standard score for each termination voltage based on the mean and standard deviation of the termination voltage. A scatter plot of the standard scores is drawn based on the standard scores of each end voltage, and the dispersion of each end voltage is analyzed based on the standard score threshold to obtain the dispersion analysis results of the target storage location.

5. The method according to claim 4, characterized in that, Based on the dispersion analysis results, abnormal negative pressure channels were identified, including: Based on the dispersion analysis results of the target storage location, determine the abnormal termination voltage where the standard score is greater than the standard score threshold; The negative voltage channel with an abnormal termination voltage is designated as the abnormal negative voltage channel.

6. The method according to claim 1, characterized in that, The risk level of each abnormal negative pressure channel is determined according to the abnormal risk assessment standard, including: Count the number of abnormal termination voltages in each negative voltage channel and the total number of abnormalities in all negative voltage channels; Calculate the abnormality rate of each negative pressure channel relative to the total number of abnormalities based on the number of abnormalities in each negative pressure channel; The risk level of each negative pressure channel is determined based on the aforementioned abnormality ratio and abnormal risk ratio range.

7. The method according to claim 6, characterized in that, Based on the risk level of the abnormal negative pressure channels, the flatness of the batteries corresponding to each abnormal negative pressure channel is measured sequentially, and the abnormal batteries are identified according to the flatness of the corresponding batteries. The process of detecting whether the negative pressure channels corresponding to the abnormal batteries are blocked includes: Batteries corresponding to abnormal negative pressure channels are selected as the batteries to be measured in descending order of risk level. Identify the plane with the largest area of ​​the battery to be measured, and select and mark test points on the plane; The height of each test point was measured using a height gauge, and the maximum height was selected as the height of the most convex point by comparison. Compare the height of the most convex point with a preset height threshold, filter out abnormal batteries whose height of the most convex point is greater than the preset height threshold, and detect whether the negative pressure channel corresponding to the abnormal battery is blocked.

8. A negative pressure channel blockage detection device based on voltage dispersion and flatness, wherein each negative pressure channel corresponds to a battery for formation experiments, and the ending voltage of the last step of each formation experiment is recorded, characterized in that, The device includes: The voltage dispersion analysis module is used to obtain multiple end voltages for each negative voltage channel, and analyze the voltage dispersion based on each end voltage to obtain the dispersion analysis results. An abnormal negative pressure channel determination module is used to determine the abnormal negative pressure channels that are abnormal based on the dispersion analysis results, and to determine the risk level of each abnormal negative pressure channel according to the abnormal risk assessment standard. The flatness measurement and blockage channel determination module is used to sequentially measure the flatness of the battery corresponding to each abnormal negative pressure channel based on the risk level of the abnormal negative pressure channel, determine the abnormal battery based on the flatness of the corresponding battery, and detect whether the negative pressure channel corresponding to the abnormal battery is blocked.

9. A computer device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1 to 7.

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