A method, apparatus, medium, and program product for pressure resistance determination based on silicon-based powder

Abstract

This invention relates to a method, equipment, medium, and procedure for determining the pressure resistance of silicon-based powder, and pertains to the field of performance testing technology for lithium-ion battery anode materials. Since the experimental and control groups of silicon-based powder samples have the same initial mass and belong to the same physical batch, the intrinsic background gas production component in the total gas production volume sequence is precisely subtracted through difference calculation. This ensures that the net gas production parameter sequence retains only the incremental gas production signal caused by mechanical pressurization and breakage. Then, the gas volume signal is converted into the mass of silicon participating in the reaction using a chemical reaction conversion coefficient. Finally, a dimensionless breakage index is constructed using the ratio of the mass of silicon participating in the reaction to the initial mass. This achieves the technical goal of completely quantifying the causal chain between macroscopic batch gas production signals and the degree of microscopic particle breakage, enabling direct horizontal comparison of the pressure resistance of silicon-based powders from different batches and with different modification processes under a unified quantitative benchmark.

Description

Technical Field

[0001] This application relates to the field of lithium-ion battery anode material performance testing technology, and in particular to a method, equipment, medium and procedure for measuring the withstand voltage performance of silicon-based powder. Background Technology

[0002] Lithium-ion batteries are widely used due to their high energy density, and silicon-based materials, as highly promising candidates for anode core materials, possess extremely high theoretical specific capacity. However, silicon-based materials undergo significant volume expansion during charging and discharging, leading to particle breakage and triggering continuous side reactions, which in turn generate gas and cause battery capacity decay. If the powder's pressure resistance is insufficient, mass breakage can easily occur during processing steps such as electrode rolling, exposing a large amount of fresh silicon surface. These fresh surfaces will react violently with electrolytes and other components in subsequent processes, generating large amounts of gas and threatening battery safety and lifespan.

[0003] In related technologies, nanoindentation testing is commonly used to characterize the mechanical compressive strength of silicon-based powders. This technique involves dispersing and fixing a small amount of silicon-based powder sample onto a hard, flat substrate. Then, using a nanoindenter probe equipped with a high-precision sensor, a gradually increasing normal load is applied to a selected individual silicon-based particle within the field of view. During the probe indentation, the device records the relationship between the load magnitude and the probe depth in real time, generating a load-displacement curve. By analyzing the yield point or fracture point of this curve, the hardness, elastic modulus, and critical breaking force of the silicon-based particle are calculated, serving as a basis for measuring the compressive strength of the silicon-based material.

[0004] However, the actual battery electrode rolling process involves a complex series of steps, including powder filling, high-pressure rolling, and post-processing stress release. During this entire process, the powder undergoes complex stress changes and secondary frictional damage. The micromechanical data obtained from related technologies are disconnected from the interfacial degradation and potential side reactions caused by powder breakage during macroscopic batch compression in practical applications. This reduces the reliability of providing quantitative guidance for the batch process optimization and safety assessment of silicon-based anode materials. Summary of the Invention

[0005] This application provides a method, apparatus, medium, and procedure for measuring the pressure resistance of silicon-based powders. These methods and procedures aim to improve the quantitative accuracy of the correlation assessment between the structural stability and chemical reactivity of silicon-based powders under macroscopic pressure conditions, thereby providing reliable quantitative guidance for the optimization of mass production processes and safety assessment of silicon-based anode materials.

[0006] In a first aspect, this application provides a method for determining the pressure resistance of silicon-based powder, applied to a performance testing device. The method includes: receiving a baseline of gas production from a control group silicon-based powder sample reacting in a preset solvent; determining a total gas production volume sequence under the discrete test pressure sequence after controlling an experimental group silicon-based powder sample and performing a simulation operation of a preset processing procedure based on a discrete test pressure sequence, wherein the experimental group silicon-based powder sample and the control group silicon-based powder sample have the same initial mass and belong to the same physical batch, and the discrete test pressure sequence is determined according to a preset step size algorithm; determining a net gas production parameter sequence based on the difference between the total gas production volume sequence and the gas production baseline; converting the net gas production parameter sequence based on a chemical reaction conversion coefficient corresponding to a preset solvent to obtain the silicon mass participating in the reaction under each discrete test pressure, and determining the ratio of the silicon mass participating in the reaction to the initial mass as the breakage degree under the corresponding discrete test pressure.

[0007] By adopting the above technical solution, since the initial mass of the silicon-based powder samples in the experimental group and the control group is the same and they belong to the same physical batch, the intrinsic background gas production component in the total gas production volume sequence is accurately deducted through difference calculation, so that the net gas production parameter sequence only retains the incremental gas production signal caused by mechanical pressure crushing. Then, the gas volume signal is converted into the silicon mass participating in the reaction through the chemical reaction conversion coefficient. Finally, a dimensionless crushing degree index is constructed by the ratio of the silicon mass participating in the reaction to the initial mass. This achieves the technical goal of completely quantifying the causal chain between the macroscopic batch gas production signal and the microscopic particle crushing degree, so that the pressure resistance of silicon-based powders of different batches and different modification processes can be directly compared horizontally under a unified quantitative benchmark.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, after determining the ratio of the mass of silicon participating in the reaction to the initial mass as the degree of fragmentation under the corresponding discrete test pressure, the method further includes: constructing a fragmentation response function of the target batch of silicon-based powder by curve fitting, using each discrete test pressure in the discrete test pressure sequence as the independent variable and each corresponding degree of fragmentation as the dependent variable, wherein the target batch of silicon-based powder includes the control group silicon-based powder sample and the experimental group silicon-based powder sample; performing a first-order derivative operation on the fragmentation response function to obtain a derivative function characterizing the rate of change of fragmentation with increasing pressure; solving for the function value of the derivative function within the range of the independent variables, marking the pressure coordinate point where the derivative function reaches its maximum value as the turning pressure value, wherein the turning pressure value characterizes the pressure threshold corresponding to the large-scale fragmentation of the target batch of silicon-based powder; and using the turning fragmentation corresponding to the turning pressure value and the turning pressure value as the pressure resistance performance judgment result of the target batch of silicon-based powder, wherein the pressure resistance performance judgment result is used for a horizontal comparison of the pressure resistance capabilities of silicon-based powders of different batches or different modification processes.

[0009] By adopting the above technical solution, a continuously differentiable breakage response function is constructed through curve fitting with each discrete test pressure as the independent variable and the corresponding breakage degree as the dependent variable. This expands the discrete data points into a continuous description of pressure resistance characteristics. Then, the first-order derivative operation is performed on the response function to obtain the derivative function. The maximum point of the derivative function physically corresponds to the critical position where the breakage activation ratio changes most drastically due to a unit pressure increment. By solving for the maximum value of the derivative function, the turning pressure value is accurately located. The turning breakage degree corresponding to the turning pressure value and the turning pressure value together constitute the pressure resistance performance judgment result. The evolution law of powder breakage behavior, which could only be described by discrete data points, is compressed into two scalar parameters with clear physical meanings. This allows the horizontal comparison of the pressure resistance of silicon-based powders of different batches or different modification processes to leap from the qualitative description level to the quantitative scalar level, improving the engineering operability of the pressure resistance performance evaluation results.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of using the breakage degree corresponding to the turning pressure value and the turning pressure value as the result of determining the pressure resistance performance of the target batch of silicon-based powder, the method further includes: obtaining the standard critical stress value corresponding to a historical batch of silicon-based powder under the same material formulation identifier as the target batch of silicon-based powder; calculating the deviation of the turning pressure value of the target batch of silicon-based powder relative to the standard critical stress value, wherein the deviation is the normalized ratio of the difference between the standard critical stress value and the turning pressure value relative to the standard critical stress value; and, if it is determined that the deviation exceeds a preset deviation threshold, generating process compensation parameters for the target batch based on the deviation through a preset deviation-process compensation mapping relationship.

[0011] By adopting the above technical solution, the standard critical stress value corresponding to the historical batch under the same material formula identification is obtained, and the normalized deviation of the current batch's turning pressure value relative to the standard critical stress value is calculated. The absolute pressure resistance performance value of the current batch is converted into a deviation measure relative to the historical benchmark, so that the amplitude of performance fluctuation between batches can be quantified in the form of dimensionless proportion. Furthermore, when the deviation exceeds the deviation threshold, the process compensation parameter generation process is automatically triggered, and the batch deviation signal at the material performance level is directly converted into compensation adjustment instructions at the process execution level. This realizes a closed-loop linkage from the pressure resistance performance measurement result to the production process response action, effectively eliminating the response lag and inaccurate compensation problems caused by relying on manual experience to judge batch performance deviation.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the step of generating process compensation parameters for the target batch based on the deviation through a preset deviation-process compensation mapping relationship specifically includes: inputting the deviation into a pre-calibrated deviation-fragmentation-filling-rate mapping table for interpolation calculation, outputting the dense filling rate corresponding to the deviation, wherein the deviation-fragmentation-filling-rate mapping table characterizes the mapping relationship between the deviation value and the proportion of the additional thickness reduction of the electrode sheet caused by particle breakage and filling of internal pores in the corresponding rolling process, wherein the dense filling rate characterizes the proportion of non-true densification thickness in the measured thickness of the electrode sheet caused by particle breakage and filling of pores; based on the standard electrode sheet thickness qualification threshold corresponding to the current rolling process, the product of the standard electrode sheet thickness qualification threshold and the dense filling rate is determined as the thickness compensation correction amount; subtracting the thickness compensation correction amount from the standard electrode sheet thickness qualification threshold to generate the electrode sheet thickness qualification threshold, and using the electrode sheet thickness qualification threshold as the thickness qualification criterion for the target batch of silicon-based powder.

[0013] By adopting the above technical solution, the deviation is input into a pre-calibrated deviation-fragmentation and pore-filling rate mapping table for interpolation calculation. The abstract pressure resistance degradation range is transformed into the proportion of the additional thickness reduction of the electrode, i.e., the dense filling rate. The thickness compensation correction amount is then determined by multiplying the dense filling rate with the standard electrode thickness qualification threshold. The correction amount is then superimposed on the standard threshold to generate the electrode thickness qualification threshold. This allows the thickness quality inspection benchmark to be adaptively adjusted according to the measured degradation degree of the pressure resistance performance of the current batch of powder. The non-true dense thickness deviation caused by particle fragmentation and pore filling is accurately separated from the quality inspection judgment, eliminating the risk of misjudgment caused by the difference in the degree of powder breakage between batches when performing thickness quality inspection with static standard threshold.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, before determining the discrete test pressure sequence, the method further includes: retrieving, based on the target supplier identification code corresponding to the silicon-based powder sample of the experimental group, a historical benchmark critical pressure matching the target supplier identification code from a historical supply database, wherein the historical benchmark critical pressure is the median of the pressure statistics corresponding to the nonlinear surge in gas production volume of historical batches of silicon powder with the same identification code when performing the same simulation test; subtracting a preset safety sinking offset from the historical benchmark critical pressure to obtain the starting test node of the discrete test pressure sequence; superimposing a preset safety buoyancy offset on the historical benchmark critical pressure to obtain the ending detection boundary of the discrete test pressure sequence, wherein the safety buoyancy offset is a detection margin preset based on the variance of the historical batch pressure distribution; within the detection interval formed by the starting test node and the ending detection boundary, controlling the pre-divided independent sub-samples from the silicon-based powder sample of the experimental group to perform pressurization tests with a preset base pressure increment, wherein the independent sub-samples are independent subsets used for exploratory calibration of the range and step size distribution of the discrete test pressure sequence; and extracting and outputting all covered test nodes within the detection interval as the discrete test pressure sequence.

[0015] By adopting the above technical solution, historical benchmark critical pressures are retrieved from the historical supply database using the target supplier identification code as an index, and used as prior anchor points. An adaptive detection interval is constructed using a safe sinking offset and a safe floating offset preset based on the variance of historical batch pressure distribution. This focuses the node distribution of the discrete test pressure sequence from the entire pressure domain to the critical pressure window where the target batch powder is most likely to exhibit large-scale fragmentation and transition behavior. Then, the effectiveness of the interval coverage is verified by performing exploratory pressure tests on independent sub-samples within the detection interval. This results in a higher node density in the transition interval of the discrete test pressure sequence used in the formal test, improving the positioning accuracy of the transition pressure value under the same constraint of the number of test nodes. At the same time, the narrowing of the detection interval reduces sample consumption and test time in non-critical pressure sections, thereby improving test accuracy and resource efficiency.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, before determining the total gas production volume sequence under the discrete test pressure sequence, and determining that the initial mass of the experimental group silicon-based powder sample and the control group silicon-based powder sample are the same and belong to the same physical batch, the method further includes: obtaining the material process identifier corresponding to the experimental group silicon-based powder sample, the material process identifier being an attribute field recording whether the silicon-based powder has undergone surface coating modification treatment during the synthesis stage; matching the material process identifier with a preset coating modification flag bit, the coating modification flag bit being a preset identification code characterizing the presence of a heterogeneous coating layer on the surface of the silicon-based powder; and, if it is determined that the material process identifier matches the coating modification flag bit, correcting the gas production volume change collected by the independent sub-sample during the pressure test to obtain gradient response parameters after eliminating pseudo-signal interference.

[0017] By employing the above technical solution, the material process identifier of the silicon-based powder samples in the experimental group is extracted and matched with the preset coating modification marker. When it is confirmed that there are heterogeneous coating layers such as carbonaceous coating, oxide coating or polymer coating on the powder surface, targeted pseudo-signal correction processing is performed on the gas production volume change collected by the pressure test of independent sub-samples. The short-time pulse-type gas production signal released by the coating layer due to local cracking at the moment of pressure is effectively separated from the original gas production data. This ensures that the gradient response parameters only reflect the continuous chemical corrosion gas production component corresponding to the actual degree of silicon core material breakage. This eliminates the interference of the coating layer pseudo-signal on the determination of the nonlinear surge point location, and reliably ensures the accuracy of the detection interval coverage verification results and the turning interval positioning of the final output discrete test pressure sequence.

[0018] In some embodiments, in conjunction with the first aspect, the step of correcting the gas production volume change collected by the independent sub-sample during the pressurization test to obtain the gradient response parameters after eliminating spurious signal interference specifically includes: acquiring the instantaneous gas production rate data continuously collected by the independent sub-sample within a preset sampling window period after the current test node has been pressurized, and generating a reaction rate time-domain waveform; determining the amplitude difference between the peak value of the reaction rate time-domain waveform and the steady-state gas production rate baseline, wherein the steady-state gas production rate baseline is the average rate corresponding to the gas production rate tending to stabilize at the end of the preset sampling window period; taking the time corresponding to the moment when the gas production rate in the reaction rate time-domain waveform first reaches a preset proportion of the difference between the steady-state gas production rate baseline and the peak value as the starting timestamp, and the time when the gas production rate falls back to the preset value after decreasing as the starting timestamp. The time corresponding to the baseline steady-state gas production rate is the termination timestamp. The time difference between the termination timestamp and the start timestamp is determined as the reaction convergence time constant. If the reaction convergence time constant is less than the stripping threshold, the current gas production is determined to be short-term pulse-type gas production. The gas production volume change is multiplied by a preset suppression coefficient to generate a reduction characterization value. The suppression coefficient is a preset constant less than 1. The stripping threshold is a preset threshold representing the upper limit of the gas production convergence time constant caused by passivation shell cracking. If the reaction convergence time constant is not less than the stripping threshold, the current gas production is determined to be continuous gas production caused by deep core structure fracture. The gas production volume change is maintained as the non-destructive characterization value. The ratio of the reduction characterization value or the non-destructive characterization value to the corresponding discrete test pressure increment is determined as the gradient response parameter.

[0019] By adopting the above technical solution, a reaction rate time-domain waveform is constructed by continuously collecting instantaneous gas production rate data within a preset sampling window period after the independent sub-samples have been compressed. The start and end timestamps are determined based on the amplitude difference between the peak value and the steady-state gas production rate baseline, and the reaction convergence time constant is calculated. The reaction convergence time constant is compared with the stripping threshold. For short-term pulse-type gas production, a reduction characterization value is generated with a suppression coefficient of less than 1. For continuous gas production caused by deep core structure fracture, the change in gas production volume is maintained as a non-destructive characterization value. The gradient response parameter after pseudo-signal correction is constructed by the ratio of the characterization value to the discrete test pressure increment. Compared with directly using the original gas production data, this method can accurately distinguish and quantitatively suppress the pseudo-signal of coating layer cracking and the real breakage signal at the time-domain feature level. This allows the gradient response parameter to reflect the real breakage activation degree of the silicon core material at each test node, improving the accuracy and repeatability of nonlinear surge point determination.

[0020] In a second aspect, this application provides a measuring device comprising: one or more processors and a memory; the memory is coupled to the one or more processors and is used to store computer program code, the computer program code including computer instructions, wherein the one or more processors invoke the computer instructions to cause the measuring device to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, this application provides a computer program product containing instructions that, when run on a measuring device, cause the measuring device to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a computer-readable storage medium including instructions that, when executed on a measuring device, cause the measuring device to perform the method described in the first aspect and any possible implementation thereof.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0024] 1. By adopting the above technical solution, a macroscopic batch gas production quantification method combining baseline subtraction of gas production from the same batch of equal-quality control group and stoichiometric conversion is used. After accurately removing the intrinsic background gas production component from the total gas production signal of the experimental group under gradient pressure through difference calculation, the net incremental gas production volume is converted into the mass of broken silicon participating in the reaction through chemical reaction conversion coefficient. The dimensionless breakage index is constructed by the ratio of this to the initial mass. This effectively solves the technical problem that micromechanical testing methods cannot truly reflect the overall breakage behavior of silicon-based powder under batch pressure conditions and the risk of derivative chemical side reactions. Thus, it realizes the accurate quantitative characterization of the correlation between the structural stability and chemical reactivity of silicon-based powder in actual electrode processing scenarios, and enables direct horizontal comparison of the pressure resistance of silicon-based powder from different batches and with different modification processes under a unified quantitative benchmark.

[0025] 2. By adopting the above technical solution, a closed-loop linkage method is used to calculate the normalized deviation based on the standard critical stress value of historical batches under the same material formula identification, and to automatically trigger the deviation-process compensation mapping to generate process compensation parameters when the deviation exceeds the threshold. This method accurately quantifies the inter-batch degradation of the current batch's pressure resistance performance as a dimensionless proportion and directly drives the output of compensation adjustment instructions at the process execution level. This effectively solves the technical problems of response lag and inaccurate compensation caused by relying on manual experience to judge batch performance deviations in related technologies. As a result, automatic closed-loop linkage from pressure resistance performance measurement results to production process compensation actions is realized, eliminating the impact of inter-batch fluctuations in the pressure resistance performance of silicon-based powder on product quality consistency.

[0026] 3. By adopting the above technical solution, the adaptive sequence narrowing method, which uses the target supplier identification code to retrieve historical benchmark critical pressure as a priori anchor point, combines safety offset to construct an adaptive detection interval, and verifies the effectiveness of interval coverage through independent sub-sample probing pressure tests, enables the node distribution of the discrete test pressure sequence to automatically focus on the critical pressure window of the large-scale fragmentation transition behavior of the target batch of powder. This effectively solves the technical problems of inefficient consumption of non-critical pressure segment resources and insufficient node density in the transition interval caused by uniformly arranging test nodes in the entire pressure domain. Thus, it achieves the technical effect of improving the positioning accuracy of the transition pressure value and the reliability of the pressure resistance performance judgment result while reducing sample consumption and test cycle. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of an applicable system architecture for a method for determining the pressure resistance of silicon-based powder according to an embodiment of this application.

[0028] Figure 2 This is a schematic flowchart of a method for determining the pressure resistance of silicon-based powder in an embodiment of this application;

[0029] Figure 3 This is another schematic flowchart of a method for determining the pressure resistance of silicon-based powder in an embodiment of this application;

[0030] Figure 4 This is an exemplary hardware structure diagram of the measuring device in an embodiment of this application.

[0031] (Explanation of reference numerals in the attached diagram) 10 Pressurization module, 20 Demolding module, 301 Transfer and packaging module, 302 Transfer and packaging module, 401 Gas generation test module, 402 Gas generation test module, and 50 Measuring equipment. Detailed Implementation

[0032] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0033] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0034] Figure 1 This is a schematic diagram of an applicable system architecture for a method for determining the pressure resistance of silicon-based powder, as provided in an embodiment of this application. (See attached diagram.) Figure 1 As shown, the system mainly includes: a pressurization module 10, a demolding module 20, a transfer and packaging module 301, a transfer and packaging module 302, a gas generation testing module 401, a gas generation testing module 402, and a measuring device 50.

[0035] To process and test the silicon-based powder samples from the experimental group and the control group separately, the system formed two parallel processing links in terms of physical or logical connection, which ultimately converged at the measuring device 50.

[0036] The processing chain for the experimental group on the left includes:

[0037] The pressure module 10 is connected to the demolding module 20: The pressure module 10 is used to apply a preset discrete test pressure (simulating the electrode rolling process) to the silicon-based powder sample of the experimental group. After the pressure is applied and held, the sample is handed over to the demolding module 20 (or connected to it through a mechanical structure) for demolding.

[0038] The demolding module 20 is connected to the transfer and packaging module 301: the demolding module 20 transfers the compressed powder after demolding to the transfer and packaging module 301. The transfer and packaging module 301 is responsible for mixing the compressed experimental samples with a preset solvent in a certain proportion and sealing them in an aluminum-plastic film.

[0039] The transfer packaging module 301 is connected to the gas generation test module 401: After the experimental group sample is packaged, it enters the gas generation test module 401, which collects the total gas generation volume data of the experimental group sample after it is crushed under constant temperature conditions.

[0040] Gas production test module 401 is connected to measuring device 50: Gas production test module 401 transmits the collected total gas production volume sequence data of the experimental group to measuring device 50.

[0041] The treatment pathway for the right-side control group includes:

[0042] The transfer packaging module 302 is connected to the gas generation test module 402: For the control group silicon-based powder sample that has not undergone pressurization treatment, it directly enters the transfer packaging module 302 and is mixed and packaged according to the same process and ratio as the experimental group. Subsequently, the sample enters the gas generation test module 402 for isothermal gas generation testing.

[0043] Gas production test module 402 is connected to measuring device 50: Gas production test module 402 transmits the collected control group “intrinsic gas production baseline” data to measuring device 50.

[0044] Measurement device 50: As the core calculation and control unit of the system, measurement device 50 is communicatively connected to gas production test module 401 and gas production test module 402 respectively. It synchronously receives gas production baseline data from the control group and total gas production volume data from the experimental group. By performing difference calculation to subtract the background gas production, and combining the chemical reaction conversion coefficient, it finally calculates and outputs the crushability and pressure resistance performance judgment results of the target batch of silicon-based powder.

[0045] Through the connection and collaboration between the above modules, the system realizes the entire process measurement from sample pressurization, packaging, gas generation testing to data analysis.

[0046] Please see Figure 2 This is a schematic flowchart of a method for determining the pressure resistance of silicon-based powder in an embodiment of this application.

[0047] S101. Receive the baseline of gas production from the reaction of the control group silicon-based powder sample in a preset solvent.

[0048] The control group of silicon-based powder samples refers to a set of baseline samples separated from the target batch of silicon-based powder. These samples are not subjected to any mechanical pressure during the entire testing process and are used solely to characterize the intrinsic gas production generated when the silicon-based powder in contact with a preset solvent under natural, unpressurized conditions. Their initial mass, particle size distribution, surface condition, and physical batch are completely identical to the subsequent experimental group of silicon-based powder samples. The only difference between the two is whether or not they have undergone a mechanical pressurization process. The control group exists to provide a precisely subtractable background value for intrinsic gas production in subsequent calculations, thereby separating the chemical gas production signal caused by the additional exposure of fresh silicon surfaces due to pressure breakage from the total gas production data. The preset solvent refers to a pre-determined liquid-phase reaction medium in the testing system used to chemically react with the silicon-based powder and generate quantitatively collectable gases. The choice of its type directly determines the reaction between silicon and the solvent. In the specific implementation scenario of this application, the reaction rate between the agents, the degree of reaction completion, and the theoretical gas generation coefficient on which subsequent stoichiometric conversion is based can be determined by the following: the preset solvent can be deionized water or a strong alkaline solution. Among them, strong alkaline solutions (such as sodium hydroxide solution or potassium hydroxide solution) are preferred in actual testing because they can react more rapidly and completely with silicon, so as to shorten the test cycle and improve the sensitivity of the gas generation signal. The gas generation baseline refers to the intrinsic gas generation volume value collected and recorded by the gas generation module after the control group silicon-based powder sample is fully reacted in contact with the preset solvent under standardized test conditions (including fixed reaction temperature and reaction duration). This value represents the background gas generation of the target batch of silicon-based powder without any external mechanical damage, only by the contact between the active sites naturally present on the powder surface and the solvent. It is the benchmark reference value used to deduct background interference in the subsequent net gas generation calculation.

[0049] Specifically, in a specific embodiment of this application, the gas volume measurement employs a non-contact, in-situ volume measurement mechanism based on Archimedes' principle of buoyancy. Its working principle is as follows: a flexible aluminum-plastic composite film encapsulation bag containing silicon-based powder and a preset solvent is suspended and completely immersed in a liquid of known density (e.g., silicone oil or deionized water) within a constant-temperature testing chamber. The suspended end of the encapsulation bag is connected to a high-precision weighing sensor. As the chemical reaction between the silicon-based powder and the preset solvent continues within the bag, the generated hydrogen gas causes the flexible encapsulation bag to continuously expand in volume. According to Archimedes' principle of buoyancy, the expansion of the encapsulation bag displaces more liquid, thereby increasing the buoyancy force it experiences within the liquid. The apparent mass change of the encapsulation bag in the liquid is collected in real time by the weighing sensor, and based on the buoyancy calculation formula, the apparent mass change signal is converted into the accumulated gas volume value within the encapsulation bag. On the one hand, it avoids the potential risks of gas leakage and dead volume error in traditional puncture or pipeline gas-conducting measurements, ensuring the absolute accuracy of the gas production baseline and total gas production volume sequence data; on the other hand, the isostatic stress state of the flexible aluminum-plastic membrane in the liquid environment can truly reflect the internal gas production expansion behavior, thus providing original data support for the subsequent calculation of net gas production parameter sequence.

[0050] The measurement equipment receives the baseline gas production data from the reaction of a control group silicon-based powder sample in a preset solvent. This process encompasses the entire chain from sample status confirmation, packaging operation specifications, reaction condition control to gas production data acquisition and statistical processing. In the sample division stage, the measurement equipment, based on the total amount of silicon-based powder in the target batch, divides it into control group samples according to a preset mass ratio. This ensures that the weighed mass of the control group is strictly aligned with the initial mass of each sub-sample in the subsequent experimental group. It is generally recommended to set up three or more parallel control group samples to reduce the impact of single weighing errors, differences in packaging operations of the transfer packaging module, and fluctuations in the accuracy of the gas production test module itself on the reliability of the baseline values. In the packaging operation stage, the control transfer packaging module 302 seals the control group sample and the preset solvent together in an aluminum-plastic composite film packaging bag according to a preset powder-solvent mass ratio (e.g., a silicon-based powder mass to solvent volume ratio of 1:5). During the reaction and collection phase, the sealed control group sample is placed in a constant-temperature test chamber. Following a preset reaction temperature (e.g., 60 degrees Celsius) and reaction duration (e.g., 24 hours), the gas production test module 402 collects the gas volume generated within the sealed bag throughout the reaction. After the reaction, the final cumulative gas production volume is read as the single measurement result. In the data processing phase, if multiple parallel control group samples are used, the measuring device calculates the arithmetic mean of the parallel measurement results. This mean is determined as the gas production baseline used in this measurement process and stored in the test data cache for retrieval in subsequent net gas production parameter sequence calculation steps.

[0051] In some embodiments, the baseline of gas production in the control group can be collected and determined in several ways: Optionally, the measuring device accurately weighs at least three control group samples from the target batch of powder according to a preset mass, and independently packages them into aluminum-plastic composite film packaging bags of the same specifications. During packaging, a strong alkaline solution is added according to a preset powder-solvent ratio. Before the heat sealing operation, residual gas in the packaging bags is removed by a mechanical exhaust fixture. Then, all packaging bags are placed in a constant temperature chamber in the same batch to complete the reaction at a preset temperature and time. After the reaction, the gas production module reads the gas production volume of each packaging bag one by one. The measuring device performs an arithmetic mean calculation on all valid readings and calculates the mean. The gas production baseline is written into the data cache. Optionally, the measuring device retrieves a pre-built solvent-baseline mapping database. This database stores intrinsic gas production baseline values ​​determined after standardized test conditions for the same physical batch of silicon-based powder under various preset solvent types. The measuring device uses the preset solvent type selected in the current test procedure as the index key to directly retrieve a matching historical baseline value from the database. After confirming that the test condition parameters corresponding to the historical baseline value are completely consistent with the current test parameters, the historical baseline value is directly used as the gas production baseline for this procedure without re-executing the laboratory packaging and gas production test operations. It is understood that a combination of real-time acquisition by online sensors and model correction can also be used to obtain the gas production baseline, which is not limited here.

[0052] S102. After performing a simulation operation of a preset processing procedure based on a discrete test pressure sequence on a silicon-based powder sample in the control experimental group, determine the total gas production volume sequence under the discrete test pressure sequence.

[0053] The experimental silicon powder sample refers to a test sample group separated from the target batch of silicon powder, belonging to the same physical batch and having the same initial mass as the control group silicon powder sample. This sample group will undergo a complete simulated processing procedure, including mechanical pressurization, demolding, and subsequent encapsulation gas generation test. The only difference between this sample group and the control group is that it needs to withstand a preset mechanical pressure before encapsulation. By applying different gradient pressures to the experimental group and collecting the corresponding gas generation data, the measuring equipment can establish a quantitative correspondence between the pressure applied and the gas generation response. The discrete test pressure sequence refers to a set of finite, discretely distributed pressure values ​​determined by a step-size algorithm within a preset pressure detection range. Each pressure node in this set corresponds to an independent experimental sub-sample, which is applied to the corresponding experimental sub-sample in sequence during the pressure resistance performance test process. The simulated operation of the preset processing procedure refers to the measuring equipment controlling the pressurization equipment and demolding equipment to perform a complete process of mechanical processing steps such as pressure forming, pressure holding, and pressure release demolding on the experimental silicon powder sample in sequence, according to the operating procedures of the actual battery electrode rolling processing conditions.

[0054] After the measuring equipment completes the acquisition of the gas production baseline of the control group and stores it in the data cache, the silicon-based powder samples of the experimental group are divided into independent sub-samples according to the number of pressure nodes contained in the discrete test pressure sequence. The weighing mass of each independent sub-sample is strictly aligned with the initial mass of the control group sample to ensure that the gas production data of each pressure node in the subsequent difference calculation is based on the same mass benchmark. After the sample division is completed, the measuring equipment performs a simulated processing procedure on each independent sub-sample in sequence. The specific operation procedure is as follows: the current sub-sample is loaded into the pressure mold, and the pressure module 10 applies a normal load to the powder according to the pressure value of the corresponding node in the discrete test pressure sequence. After reaching the target pressure, the preset holding time is maintained to simulate the holding pressure state in the actual rolling process. Then, the mold release module 20 is controlled to remove the powder after the pressure holding is completed from the mold. The mold release operation must be performed according to the standardized release rate and release direction to ensure that the degree of secondary friction damage experienced by each pressure node sub-sample during the mold release process is repeatable and consistent. After demolding, the measuring equipment controls the transfer and packaging module 301 to transfer the extracted powder into an aluminum-plastic composite film packaging bag. A preset solvent is added according to the same powder-to-solvent mass ratio as the control group (e.g., a silicon-based powder mass to solvent volume ratio of 1:5). Before heat sealing, residual gas in the packaging bag is removed using an venting fixture to complete the sealing operation. After packaging, the corresponding packaging bag is placed in a constant-temperature testing chamber. The measuring equipment activates the gas generation testing module 401 at the same reaction temperature (e.g., 60 degrees Celsius) and reaction duration (e.g., 24 hours) as the control group to collect the cumulative gas generation volume in the packaging bag. After the reaction, the total gas generation volume value corresponding to this subsample is read and written into the corresponding pressure node position in the total gas generation volume sequence. The complete operation process of applying pressure, demolding, transferring and packaging, and gas generation testing described above is executed independently and repeatedly for each pressure node in the discrete test pressure sequence. After all sub-samples corresponding to all pressure nodes have completed the gas generation test, the measuring device arranges all the collected gas generation volume values ​​in ascending order of pressure nodes and outputs a complete total gas generation volume sequence for use in the subsequent calculation steps of the net gas generation parameter sequence.

[0055] Understandably, it is also possible to directly perform a single simulated processing operation on the silicon-based powder samples of the experimental group based on the single pressure node corresponding to the most recent processing step and collect the corresponding total gas production volume, without constructing a complete discrete test pressure sequence. Specifically, the measuring equipment reads the pressure value corresponding to the most recent actual rolling process experienced by the current batch of silicon-based powder from the production execution system or process parameter records, uses this pressure value as the only test node, controls the pressurizing equipment to apply this pressure to the experimental group samples and maintains it for a preset holding time, and then the demolding equipment removes the pressurized powder, and after transfer and packaging, the gas production module collects the total gas production volume of this single node under the same temperature and duration conditions as the control group. At this time, the total gas production volume sequence degenerates into a single value, and the net gas production parameter sequence and the fragmentation calculation also degenerate into single-point results. This method is suitable for online quality inspection scenarios on production lines, where it is not necessary to fully characterize the complete pressure resistance characteristic curve of the target batch of powder, but only to quickly assess whether the pressure resistance of the current batch of powder under actual processing pressure conditions meets the process qualification standards. By comparing the single-point breakage degree with the preset qualified breakage degree threshold, the measuring equipment can quickly determine whether the current batch of powder is suitable for the current rolling process parameters, shortening the testing cycle and reducing sample consumption. No limitations are imposed here.

[0056] S103. Determine the net gas production parameter sequence based on the difference between the total gas production volume sequence and the gas production baseline.

[0057] After the measuring equipment completes the gas production collection of all pressure node sub-samples in the experimental group and outputs the total gas production volume sequence, the process enters the calculation stage of the net gas production parameter sequence. The core task of this stage is to accurately separate the aliased intrinsic gas production background and the additional gas production signal of the breakage in the total gas production volume sequence of the experimental group through standardized difference calculation, and output the net gas production parameter sequence that only reflects the pressure breakage effect.

[0058] Specifically, the measuring device retrieves the gas production baseline value determined and stored in step S101 and the total gas production volume sequence established in step S102 from the data buffer. For each pressure node data point in the total gas production volume sequence, it performs point-by-point difference calculation with the gas production baseline value as a fixed subtrahend. The resulting differences are arranged in ascending order according to the corresponding pressure nodes to construct the net gas production parameter sequence and write it into the data buffer. Before performing the difference calculation, the measuring device needs to jointly verify the validity of the total gas production volume sequence and the gas production baseline data. The specific checks include: whether the values ​​of each node in the total gas production volume sequence are all greater than the gas production baseline. If the total gas production volume value corresponding to a certain pressure node is less than or equal to the gas production baseline, the measuring device should mark the net gas production difference corresponding to that node as an abnormal data point and trigger the retest process of the subsample at that node. This is because, under normal physical logic, the total gas production of the experimental group sample that has undergone mechanical pressurization should not be lower than the baseline value of the unpressurized control group. If a reverse difference occurs, it usually means that there is a slight leakage in the sealing bag at that node, the reading of the gas production measuring device is abnormal, or contamination has occurred during the sample transfer process. The device also checks the monotonicity of the net gas production parameter sequence. That is, as the discrete test pressure increases, the net gas production difference should generally show a non-decreasing trend. If a non-monotonic drop data point appears in the sequence, the measuring device should mark the data point and perform interpolation correction or retest decision in combination with the data of adjacent nodes. After completing the data validity verification, the measuring equipment performs standardized difference calculation on all pressure nodes that have passed the verification, and stores the final net gas production parameter sequence along with the corresponding pressure node coordinates into the data cache for retrieval in step S104.

[0059] In some embodiments, the determination of the net gas production parameter sequence can be achieved in several ways: Optionally, the measuring device sequentially reads the node values ​​of the total gas production volume sequence from the data buffer, performs a subtraction operation on each node value with the gas production baseline as a fixed subtrahend, and outputs the resulting differences in ascending order of pressure nodes as the net gas production parameter sequence. Before output, a non-negativity test is performed on each difference data point. Data points that fail the test are marked as nodes to be retested and temporarily replaced by the linear interpolation result of the difference between adjacent nodes. After the retest data is available, the interpolation result is replaced by the measured difference, thus completing the determination of the net gas production parameter sequence. The final output of the gas production sequence; optionally, before performing the difference calculation, the measuring equipment performs an intra-batch consistency reassessment of the gas production baseline, recalculates the outlier removal and mean of the gas production volume readings of each parallel sample in the control group, and uses the updated mean as the final gas production baseline for the difference calculation. Then, the final gas production baseline is subtracted from each node value in the total gas production volume sequence, the difference sequence is sorted in ascending order by pressure node and a monotonicity test is performed, and local retesting or interpolation correction is triggered for data points that fail the monotonicity test, finally outputting a net gas production parameter sequence that has passed data quality verification. It is understood that a combination of sliding window smoothing filtering and difference calculation can also be used, first performing sliding mean smoothing on the total gas production volume sequence to suppress measurement noise, and then performing difference calculation with the gas production baseline as the subtrahend to output the net gas production parameter sequence, which is not limited here.

[0060] S104. The net gas production parameter sequence is converted based on the chemical reaction conversion coefficient corresponding to the preset solvent to obtain the silicon mass participating in the reaction under each discrete test pressure, and the ratio of the silicon mass participating in the reaction to the initial mass is determined as the degree of fragmentation under the corresponding discrete test pressure.

[0061] The chemical reaction conversion coefficient is a proportionality coefficient, pre-calibrated under standardized test conditions, that converts the gas production per unit volume into the corresponding mass of silicon participating in the reaction, based on the stoichiometric relationship between silicon-based powder and a preset solvent. The value of this coefficient is determined by the type of preset solvent and the temperature and pressure conditions of the test environment. When the preset solvent is deionized water, silicon reacts with water to produce hydrogen gas. After correction under actual test conditions, the theoretical gas production volume per gram of silicon is approximately 1.7 liters, corresponding to a chemical reaction conversion coefficient of approximately 0.588 mg of silicon per milliliter of hydrogen. When the preset solvent is a strong alkaline solution (e.g., sodium hydroxide solution), silicon also reacts with the strong alkali to produce hydrogen gas. Theoretically, each gram of silicon produces approximately 1.6 liters of hydrogen gas, corresponding to a chemical reaction conversion coefficient of approximately 0.625 mg of silicon per milliliter of hydrogen. Strong alkaline solvents are preferred in actual tests due to their faster reaction rate and more complete reaction.

[0062] After the measuring equipment completes the calculation of the net gas production parameter sequence and stores it in the data cache, the process enters the stoichiometric conversion and fragmentation calculation stage. The core task of this stage is to convert the incremental gas production data in the net gas production parameter sequence, which is in units of gas volume, into fragmented silicon mass data in units of mass, based on the theoretical stoichiometric relationship between the preset solvent and silicon-based material. On this basis, the fragmentation value sequence corresponding to each discrete test pressure node is output through normalization processing. Specifically, when performing this step, the measuring device reads the preset solvent type selected for the current test process from the test parameter configuration, and retrieves the corresponding conversion coefficient value from the chemical reaction conversion coefficient database using the solvent type as the index key. After confirming that the conversion coefficient matches the temperature and pressure conditions of the current test environment, the measuring device retrieves the net gas production parameter sequence from the data cache. For the net gas production volume value corresponding to each discrete pressure node in the sequence, a point-by-point conversion operation is performed with the chemical reaction conversion coefficient as the divisor to convert the net gas production volume into the mass of silicon participating in the reaction corresponding to that node. The specific conversion logic is as follows: divide the net gas production volume value by the theoretical gas production volume per unit mass of silicon represented by the chemical reaction conversion coefficient to obtain the absolute mass value of silicon exposed and participating in the chemical reaction due to pressure breakage at that pressure node. After completing the conversion of the reacting silicon mass at all pressure nodes, the measuring device further reads the initial mass value of the silicon-based powder sample of the experimental group from the test parameter record, performs a division operation on the reacting silicon mass value corresponding to each pressure node and the initial mass value, determines the resulting ratio as the breakage value corresponding to that pressure node, and arranges all breakage values ​​in ascending order according to the pressure nodes, constructs a breakage sequence and outputs it to the data cache.

[0063] In some embodiments, the conversion from net gas production parameter sequence to fragmentation sequence can be achieved in multiple ways: Optionally, the measuring device reads the corresponding chemical reaction conversion coefficient from a pre-stored conversion coefficient configuration table using a preset solvent type as the key value, sequentially performs a division operation on the net gas production volume value of each node in the net gas production parameter sequence to obtain the corresponding silicon mass participating in the reaction, then performs a division operation on the silicon mass participating in the reaction of each node with the initial mass of the experimental group to obtain the corresponding fragmentation degree, performs an interval rationality check on all fragmentation degree values, and outputs the values ​​that pass the check in ascending order of pressure node as the fragmentation degree sequence, and triggers source tracing for abnormal nodes that fail the check. The process involves verifying data validity and then supplementing and correcting values ​​to complete the final output of the breakage sequence. Optionally, before performing stoichiometric conversion, the measuring equipment performs a single-point calibration test using the same type of standard silicon powder used in the current batch of tests. The ratio of the measured gas production volume to the theoretical gas production volume in the calibration test is used as a correction factor to update the pre-stored conversion coefficients. Subsequently, the updated conversion coefficients are used to perform point-by-point conversion on the net gas production parameter sequence. After obtaining the silicon mass involved in the reaction at each node, a normalized division operation is performed with the initial mass to output the calibrated and corrected breakage sequence, ensuring that the conversion coefficients have optimal metrological accuracy under the actual environmental conditions of the current test batch. It is understood that a multi-solvent parallel conversion coefficient matrix approach can also be used. For scenarios where multiple solvent types may be involved in the test process, the measuring equipment pre-stores a matrix configuration containing conversion coefficients for various solvents such as water and strong alkalis. The conversion calculation is automatically performed based on the corresponding conversion coefficient according to the solvent type selected in the current test. This is not limited here.

[0064] In some embodiments, after determining the ratio of the mass of silicon participating in the reaction to the initial mass as the breakage degree under the corresponding discrete test pressure, the measuring device constructs a breakage degree response function for the target batch of silicon-based powder using a curve fitting algorithm, with each discrete test pressure in the discrete test pressure sequence as the independent variable and the corresponding breakage degree as the dependent variable. Specifically, the measuring device retrieves the breakage degree sequence and the corresponding pressure node coordinates from the data cache, selects an appropriate function model (e.g., a piecewise polynomial function, a sigmoid-type growth function, or a spline interpolation function) to perform least squares fitting on the discrete data points, and outputs a continuously differentiable breakage degree response function. This function can provide a corresponding breakage degree prediction value at any pressure value within the range of the independent variable, thereby expanding the discrete pressure-breakage degree data points into a continuous description of pressure resistance characteristics. After obtaining the fragmentation response function, the measuring equipment performs a first-order derivative operation on the function to obtain a derivative function characterizing the rate of change of fragmentation with increasing pressure. This derivative function physically represents the change in the activation ratio of silicon-based powder fragmentation caused by a unit pressure increment. The pressure coordinate point where the derivative function reaches its maximum value corresponds to the critical transition position where the powder fragmentation behavior jumps from gradual evolution to large-scale fragmentation. The measuring equipment solves for all function values ​​of the derivative function within the range of independent variables, marking the pressure coordinate point where the derivative function reaches its maximum value as the transition pressure value. This transition pressure value is then substituted into the fragmentation response function to obtain the corresponding transition fragmentation. The transition pressure value and the transition fragmentation are combined as the pressure resistance performance judgment result of the target batch of silicon-based powder, for use in the horizontal comparison of the pressure resistance capabilities of silicon-based powders from different batches or with different modification processes.

[0065] In other embodiments, the measuring device may determine the turning pressure value without performing a first-order derivative of the continuous fitting function and searching for the maximum value. Instead, it may employ a discrete maximum search method based on the difference between adjacent nodes in the breakage sequence. Specifically, the measuring device calculates the ratio of the difference in breakage values ​​between adjacent pressure nodes in the breakage sequence to the corresponding pressure increment, constructs a discrete difference quotient sequence, searches for the node position where the maximum value is obtained in the discrete difference quotient sequence, directly marks the pressure value corresponding to that node as the discrete turning pressure value, and marks the corresponding breakage value as the turning breakage. This serves as an approximate output of the pressure resistance performance judgment result for the target batch of silicon-based powder. This method does not require function fitting and differentiation operations, has lower computational complexity, and is suitable for online quality inspection scenarios with high real-time requirements. No further limitations are imposed here.

[0066] In some embodiments, after using the breakage degree corresponding to the turning pressure value and the turning pressure value as the results of judging the pressure resistance performance of the target batch of silicon-based powder, the measuring device obtains the standard critical stress value corresponding to the historical batch of silicon-based powder under the same material formulation identifier as the target batch of silicon-based powder, and calculates the deviation of the turning pressure value of the target batch of silicon-based powder from the standard critical stress value. Specifically, the measuring device retrieves all historical batch measurement records belonging to the same formulation system as the current target batch from the historical performance database using the material formulation identifier as the index key, extracts the statistical median of the turning pressure value dataset of each historical batch as the standard critical stress value, divides the difference between the standard critical stress value and the current batch turning pressure value by the standard critical stress value, and obtains the normalized deviation value. This value quantifies the degradation of the pressure resistance performance of the current batch of powder relative to the historical benchmark in the form of a dimensionless ratio. If the deviation exceeds the preset deviation threshold, the measuring device determines that the pressure resistance performance of the current batch of powder has degraded between batches, triggering the generation process of process compensation parameters. Based on the deviation, the device outputs process compensation parameters for the target batch through the preset deviation-process compensation mapping relationship, so that the process parameters can be adjusted in a targeted manner in subsequent production processes to eliminate the impact of batch-to-batch pressure resistance performance fluctuations on product quality consistency.

[0067] In other embodiments, the measuring device may not use the statistical median of historical batch turning pressure values ​​as the standard critical stress value. Instead, it may extract the time-weighted average of historical batch turning pressure values ​​under the same material formulation identifier from the historical performance database. The weighting coefficient is the degree of proximity between each historical batch and the current batch in terms of production time. The more recent historical batch is assigned a higher weight, and the more distant historical batch is assigned a lower weight. In this way, a standard critical stress value that can adaptively reflect the recent process fluctuation trend is constructed. This standard critical stress value is used instead of the static statistical median in the deviation calculation, so that the deviation assessment result has higher sensitivity and timely response to recent changes in the production process status. This is not limited here.

[0068] Specifically, the measuring equipment inputs the deviation into a pre-calibrated deviation-fragmentation-filling-pore ratio mapping table for interpolation calculation, outputting the dense filling rate corresponding to the deviation. This deviation-fragmentation-filling-pore ratio mapping table was pre-calibrated through numerous historical batches of comparative experiments. It records the correspondence between the percentage reduction in electrode thickness caused by silicon-based powder particles breaking and filling the internal pores of the electrode during the rolling process at different deviation levels and the deviation value. The mapping table uses the deviation as the horizontal axis index and the dense filling rate as the vertical axis output. For deviation values ​​not directly covered in the mapping table, the measuring equipment calculates the corresponding dense filling rate using linear interpolation or spline interpolation methods between adjacent calibration points. After obtaining the dense filling rate, the measuring equipment reads the standard electrode thickness qualification threshold corresponding to the current rolling process from the process parameter database. The product of this standard threshold and the dense filling rate is determined as the thickness compensation correction amount. This correction amount physically represents the false reduction in electrode thickness introduced by the increased particle breakage and additional pore filling caused by the degradation of the current batch of powder's pressure resistance. Subsequently, the measuring equipment superimposes a thickness compensation correction on the standard electrode thickness qualification threshold to generate an electrode thickness qualification threshold. This threshold is then sent to the production execution system as the thickness qualification benchmark for the rolling process of the target batch of silicon-based powder, replacing the original static standard threshold. This ensures that the thickness quality inspection link of the rolling process can accurately identify the thickness measurement deviation caused by the performance fluctuation of the powder batch, and avoid quality inspection misjudgment caused by false densification effect.

[0069] It should be noted that the above deviation-fragmentation pore filling rate mapping table is not a fixed parameter table preset based on experience, but a quantitative correspondence database established through multiple batches of comparative calibration experiments. The specific calibration method is as follows:

[0070] During the calibration sample selection phase, the measuring equipment selects several representative target batches of silicon-based powder from historical archives, covering a wide range of deviations. The deviation values ​​of the selected batches are required to be evenly distributed within the target mapping range. Typically, at least eight calibration batches are selected, covering deviations ranging from 5% to 40% at 5% intervals, to ensure the mapping table has sufficient interpolation accuracy within the target deviation range. For each calibration batch, the measuring equipment calculates its deviation value according to the complete measuring procedure from S101 to S104, and uses the calculation result as the index coordinate of that calibration batch on the horizontal axis of the mapping table. During the electrode preparation and thickness measurement stages, two sets of control electrodes were prepared for each batch of silicon-based powder: the first set consisted of initial electrodes without roll forming, whose preparation process strictly followed the standard coating and drying process, and the average thickness of the initial electrodes was used as the baseline electrode thickness for this batch; the second set consisted of rolled electrodes after a standard roll forming process, with the roll forming pressure set to the same standard roll forming pressure as the actual production line. After roll forming, the thickness of the rolled electrodes was measured at multiple points and the average value was taken as the measured thickness of the rolled electrodes for this batch. During the thickness measurement process, a high-precision contact thickness gauge was used to collect thickness data at at least nine evenly distributed measurement points on each electrode, and the average value of the nine points was taken as the representative thickness of the electrode to eliminate the influence of uneven electrode thickness distribution on the calibration results. In the calculation stage of the fragmentation and pore-filling rate, for each calibration batch, the measuring equipment calculates the theoretical electrode thickness after rolling under the condition that the silicon-based powder particles remain intact and do not break, based on the initial electrode porosity parameters and the theoretical compression ratio under standard rolling pressure. This theoretical thickness represents the expected electrode thickness that the rolling process should produce without the fragmentation and pore-filling effect. Subsequently, the measuring equipment compares the theoretical electrode thickness after rolling with the measured electrode thickness after rolling, and divides the difference between the two by the theoretical electrode thickness after rolling. The resulting ratio is the fragmentation and pore-filling rate of the calibration batch under standard rolling pressure. This ratio quantitatively characterizes the proportion of the additional false reduction in electrode thickness caused by the breakage of silicon-based powder particles and the filling of electrode pores during rolling to the theoretical electrode thickness after rolling.

[0071] In the mapping relationship fitting stage, after completing the pairwise numerical calculation of deviation and fragmentation filling rate for all calibration batches, the measuring equipment performs curve fitting on all calibration data points, selecting an appropriate function model to parametrically fit the quantitative correspondence between deviation and fragmentation filling rate. Based on the typical distribution characteristics of the calibration experimental data, the deviation and fragmentation filling rate usually exhibit an approximately linear or weakly nonlinear monotonically increasing relationship. Within the moderate deviation range of 5% to 25%, the goodness of fit between the two is usually above 0.92, and a first-order linear regression model can be used for direct fitting. In the high deviation range exceeding 25%, the growth rate of fragmentation filling rate may exhibit nonlinear acceleration; in this case, a second-order polynomial or power function model should be used to improve fitting accuracy. Taking a typical set of calibration experimental data as an example, when the deviations of eight calibration batches are 5%, 10%, 15%, 20%, 25%, 30%, 35%, and 40% respectively, the corresponding measured calibration values ​​of fragmentation and pore filling rates are approximately 0.8%, 1.6%, 2.4%, 3.2%, 4.3%, 5.7%, 7.4%, and 9.6% respectively. A linear regression fit is performed on this dataset within the range of 5% to 25%, yielding a linear relationship where the fragmentation and pore filling rate is approximately equal to 0.16 multiplied by the deviation, with a goodness of fit R² of 0.994. A second-order polynomial fit is then performed on the data points within the range of 25% to 40%, yielding the corresponding nonlinear segment mapping relationship. The piecewise fitting results of the linear and nonlinear segments are combined to form a complete deviation-fragmentation and pore filling rate mapping function. Based on this mapping function, a discretized deviation-fragmentation and pore filling rate mapping table is generated according to a preset deviation resolution (e.g., 0.5% step size) and stored in the mapping table database of the measuring equipment for interpolation retrieval in the actual measuring process.

[0072] It should be further explained that the deviation-fragmentation and pore filling rate mapping table is not a one-time static parameter. After a certain number of actual production batches are measured, the measuring equipment should include the deviation of the new batch and the corresponding measured electrode thickness data into the mapping table update process. By continuously expanding and refitting the historical calibration dataset, the interpolation accuracy of the mapping table in each deviation range is continuously corrected, so that the mapping table continuously approaches the real physical law of fragmentation and pore filling as the measurement data accumulates. This ensures that the generation of the electrode thickness qualification threshold is always based on statistically representative calibration data.

[0073] In this embodiment, the incremental gas generation signal caused by the exposure of fresh silicon surface due to particle breakage during the simulated processing of silicon-based powder is converted into a quantitative evaluation result with the degree of breakage as the core indicator by the difference calculation and stoichiometric conversion with the gas generation baseline of the control group. This effectively solves the technical problem that existing micromechanical testing methods such as nanoindentation cannot truly reflect the breakage behavior of silicon-based powder and its derived side reaction risks under macroscopic batch pressure conditions. This enables the quantitative characterization of the correlation between the structural stability and chemical reactivity of silicon-based powder in actual electrode processing scenarios, providing a reliable quantitative guidance for the batch process optimization and safety assessment of silicon-based anode materials.

[0074] In the above embodiments, the measuring device can evaluate the pressure resistance performance of a target batch of silicon-based powder using the aforementioned fragmentation determination method based on gas production quantification. In practical applications, when performing the pressure resistance performance measurement method, if the measuring device directly sets a discrete test pressure sequence with uniform step sizes across the entire pressure range, it may result in excessive sample size and testing resources being invested in pressure segments far from the actual critical fragmentation range of the powder, while the pressure node density within the critical transition range is insufficient, limiting the accuracy of the fragmentation response function in the most engineering-significant fragmentation transition region. This pressure resistance performance measurement method can solve this technical problem by incorporating an adaptive narrowing mechanism of the detection range based on the historical benchmark critical pressure of the target supplier. This allows the distribution range and node density of the discrete test pressure sequence to automatically focus on the pressure range where the target batch of powder is most likely to experience large-scale fragmentation, reducing sample consumption and testing cycle while improving the accuracy of the pressure resistance characteristic curve within the critical range.

[0075] Please see Figure 3 This is another schematic flowchart of a method for determining the pressure resistance of silicon-based powder in an embodiment of this application.

[0076] S201. Receive the baseline of gas production from the reaction of the control group silicon-based powder sample in a preset solvent.

[0077] Step S201 and Figure 2 Step S101 in the illustrated embodiment is similar and can be found in the description of step S101 in section 1, which will not be repeated here.

[0078] S202. Based on the target supplier identification code corresponding to the silicon-based powder sample in the experimental group, retrieve the historical benchmark critical pressure that matches the target supplier identification code from the historical supply database.

[0079] The target supplier identification code is a structured code field uniquely assigned to each silicon-based powder supplier within the material management system of the testing equipment. This code field not only identifies the supplier's identity but also implicitly links to upstream factors affecting the powder's mechanical properties, such as the specific silicon-based powder synthesis process, raw material ratio system, and quality control specifications used by the supplier. Therefore, different batches of silicon-based powder with the same target supplier identification code have a high degree of homology in material system and preparation process, and their critical pressure distribution is statistically comparable. The historical supply database refers to the historical withstand pressure test records stored in the testing equipment or its connected data management system, indexed and organized according to multiple dimensions such as the target supplier identification code, material formulation identifier, and test batch timestamp. The database records the pressure statistics corresponding to the nonlinear surge in gas production of historical batches of silicon-based powder under each supplier's identification code when performing the same simulated test process. It is the core data source for obtaining prior pressure information in this step. The historical benchmark critical pressure refers to the statistical median of the pressure values ​​corresponding to the nonlinear surge in gas production of all historical batches of silicon-based powder matching the target supplier's identification code when performing the same simulated processing test process, retrieved from the historical supply database. This median comprehensively reflects the typical large-scale fragmentation initiation pressure level of the powder from this supplier in historical tests in a robust statistical manner. Compared with the mean, it is less sensitive to extreme abnormal batch data and can more stably represent the core of the mechanical pressure resistance characteristics of the powder from this supplier.

[0080] After the measuring equipment completes the acquisition of the gas production baseline of the control group and stores it in the data cache, before formally performing gradient pressure testing on the silicon-based powder samples of the experimental group, the measuring equipment enters the adaptive positioning stage of the detection range based on historical prior information. The core task of this stage is to use historical batch test data from the same source as the target batch of powder to provide statistically based prior pressure reference values ​​for constructing the range of discrete test pressure sequences, avoiding the waste of test resources and insufficient accuracy in characterizing key intervals caused by blindly and uniformly distributing points in the entire pressure domain.

[0081] Specifically, the testing equipment extracts the target supplier identification code from the material information record of the target batch of powder. Using this identification code as the primary index key, it initiates a search request to the historical supply database. After responding to the search request, the historical supply database filters out all historical batch test records that completely match the target supplier identification code from its stored records. It then extracts the pressure value dataset for each historical batch where the gas production rate exhibits a non-linear surge during the same simulated processing procedure. The statistical median is calculated on this dataset, and the result is output as the historical baseline critical pressure to the data cache of the testing equipment. Before performing the statistical median calculation, the testing equipment should perform outlier pre-screening on the historical pressure dataset to identify and remove extreme data points that deviate from the normal distribution range due to testing equipment malfunction, sample contamination, or operational errors. This ensures that the statistical representativeness of the historical baseline critical pressure is not affected by individual abnormal batches.

[0082] In some embodiments, the retrieval and determination of historical baseline critical pressure can be achieved in several ways: Optionally, the measuring device parses the target supplier identification code from the target batch powder material information, uses the identification code as an index key to initiate a precise matching search to the historical supply database, extracts the pressure value corresponding to the nonlinear surge in gas production of each batch from the returned historical batch record set, performs interquartile range outlier removal processing on the dataset, determines the statistical median of the remaining valid data points as the historical baseline critical pressure, and writes the result into the data cache for use in subsequent steps; Optionally, based on the precise matching of the target supplier identification code, the measuring device further introduces the material formulation identifier as a secondary screening condition, retrieves historical batch records from the historical supply database that simultaneously meet the dual matching conditions of the target supplier identification code and the target material formulation identifier, performs statistical median calculation on the more refined dataset after screening, and outputs the historical baseline critical pressure that simultaneously reflects the characteristics of the supplier source and the characteristics of the formulation system. Compared with searching only based on the supplier identification code, this method has higher targeting and accuracy. When the target supplier provides silicon-based powders with multiple different formulation systems, this method can effectively avoid the confusion between critical pressure data of powders with different formulation systems. It is understandable that a historical critical pressure prediction method based on machine learning regression models can also be used. This method uses material characteristic parameters such as target supplier identification code, material formula identification, powder particle size distribution parameters and surface area ratio as input features, and uses the critical surge pressure measured in historical batches as training labels to train a regression prediction model. The model then outputs the estimated historical baseline critical pressure value for the current batch through inference. No limitation is imposed here.

[0083] S203. Subtract the preset safe sinking offset from the historical benchmark critical pressure to obtain the starting test node of the discrete test pressure sequence, and superimpose the preset safe buoyancy offset on the historical benchmark critical pressure to obtain the ending detection boundary of the discrete test pressure sequence.

[0084] Among them, the safe sinking offset refers to a fixed offset margin, pre-set based on the statistical characteristics of historical batch pressure distribution, extending from the historical benchmark critical pressure towards low pressure. The specific value is usually determined based on the left interquartile range of the historical batch critical pressure distribution. The safe buoyancy offset refers to a detection margin, pre-set based on the variance of the historical batch pressure distribution, extending from the historical benchmark critical pressure towards high pressure. This margin is usually determined based on the right interquartile range of the historical batch critical pressure distribution or a pre-set multiple of the historical distribution variance. The starting test node refers to the pressure value obtained by subtracting the safe sinking offset from the historical benchmark critical pressure. The lowest pressure test starting point in the discrete test pressure sequence indicates that the target batch of powder is expected to be less fragmented at this pressure level, resulting in a weaker net gas production signal, but still within the effective detection range of the gas production test module. The termination detection boundary refers to the pressure value obtained after superimposing the historical benchmark critical pressure with the safety upward offset, representing the highest pressure test endpoint in the discrete test pressure sequence. At this pressure level, the target batch of powder is expected to have undergone sufficient large-scale fragmentation, resulting in a higher net gas production signal, ensuring sufficient data coverage of the fragmentation response function in the high-damage region above the inflection point.

[0085] After the measuring equipment successfully retrieves the historical benchmark critical pressure from the historical supply database and stores it in the data cache, the measuring equipment reads the historical benchmark critical pressure value from the data cache. At the same time, it reads the current set values ​​of the safe sinking offset and the safe buoyancy offset from the preset parameter configuration table. It performs subtraction and addition operations on the historical benchmark critical pressure respectively. The result of subtracting the safe sinking offset from the historical benchmark critical pressure is determined as the starting test node of the discrete test pressure sequence. The result of superimposing the safe buoyancy offset on the historical benchmark critical pressure is determined as the ending detection boundary. The calculation results of the starting test node and the ending detection boundary are synchronously written into the data cache to form the detection interval boundary parameter pair for use in step S204. After performing boundary calculations, the measuring equipment should also verify the rationality of the obtained starting test node and ending detection boundary. Specific checks include: whether the starting test node is greater than zero and not lower than the minimum pressurizable lower limit of the pressurizing equipment; whether the ending detection boundary does not exceed the rated maximum pressure upper limit of the pressurizing equipment; and whether the width of the interval between the starting test node and the ending detection boundary is sufficient to accommodate at least the minimum number of preset test nodes multiplied by the minimum interval span required for the base pressure increment. If any of the above rationality checks fails, the measuring equipment should automatically adjust the corresponding offset parameters or issue a warning to the operator, requesting manual confirmation before continuing with subsequent steps. It should be further noted that the safe sinking offset and safe buoyancy offset are not fixed constants. The measuring equipment should support adaptive updates of the two offset parameters based on changes in the variance of the critical pressure distribution of historical batches from the target supplier. When the variance of the critical pressure distribution of historical batches is large, the two offsets should be automatically expanded to ensure sufficient coverage redundancy in the detection interval. When the variance of the critical pressure distribution of historical batches is small and the performance consistency between batches is high, the two offsets should be automatically narrowed to further focus the detection interval and increase the node density in key areas.

[0086] In some embodiments, the determination of the starting test node and the ending detection boundary can be achieved in several ways: Optionally, the measuring device reads fixed safe sinking offset and safe buoyancy offset values ​​from a preset parameter configuration table, performs subtraction and addition operations with historical benchmark critical pressures respectively, and directly determines the starting test node and the ending detection boundary as the calculation results. Subsequently, the two boundary values ​​are checked for the rationality of the pressure range of the pressurizing device. If the check passes, the boundary parameter pair is written to the data cache; if the check fails, the boundary values ​​are corrected to the most recent effective pressure value within the rated range of the pressurizing device before being written to the data cache. Optionally, the measuring device reads fixed safe sinking offset and safe buoyancy offset values ​​from a preset parameter configuration table, performs subtraction and addition operations with historical benchmark critical pressures respectively, and directly determines the starting test node and the ending detection boundary as the calculation results. Then, the pressure range of the pressurizing device is checked for rationality of the pressure range of the pressurizing device. If the check passes, the boundary parameter pair is written to the data cache; if the check fails, the boundary values ​​are corrected to the most recent effective pressure value within the rated range of the pressurizing device before being written to the data cache. The system retrieves all historical batch critical pressure datasets corresponding to the target supplier's identifier from the historical supply database. It calculates the left and right interquartile ranges of this dataset. The left interquartile range is multiplied by a preset subsidence ratio coefficient to determine the safe subsidence offset, and the right interquartile range is multiplied by a preset buoyancy ratio coefficient to determine the safe buoyancy offset. The safe subsidence offset and the superimposed safe buoyancy offset are then subtracted from the historical baseline critical pressure to obtain the adaptively narrowed starting test node and ending detection boundary. Compared to the fixed offset scheme, this method allows the detection interval width to automatically adjust adaptively according to the dispersion of the historical batch pressure distribution. It is understood that a Bayesian interval estimation method can also be used, constructing a critical pressure probability distribution model using the historical batch critical pressure dataset as prior information, and using the lower and upper limits of the confidence interval corresponding to the preset confidence level as the starting test node and ending detection boundary, respectively. This is not limited here. It should be noted that in actual testing scenarios, if the target batch of silicon-based powder has undergone a different surface modification treatment than historical batches (e.g., the addition of carbon coating or oxide coating processes), the measuring equipment should identify the direction of the influence of the modification treatment on the critical pressure of the powder. When calculating the starting test node and the ending detection boundary, the corresponding modification correction bias should be applied to the historical reference critical pressure to avoid the detection range deviating from the true turning pressure range of the target batch due to ignoring the differences in the modification process.

[0087] S204. Within the detection range formed by the starting test node and the ending detection boundary, control the pre-divided independent sub-samples from the silicon-based powder samples in the experimental group to perform pressure testing with a preset base pressure increment.

[0088] The detection interval refers to the continuous pressure range defined by the starting test node and the ending detection boundary. This interval represents the most likely pressure window for the target batch of silicon-based powder to undergo large-scale fragmentation and turning behavior based on historical prior information. The existence of the detection interval focuses the node distribution of the discrete test pressure sequence from the entire pressure domain to the critical pressure segment with the greatest engineering value, which is the core design for achieving efficient utilization of test resources. The independent subsample refers to an independent subset pre-divided from the total amount of silicon-based powder samples in the experimental group, which is specifically used for exploratory calibration of the range and step size distribution of the discrete test pressure sequence before formal testing. This subset is physically isolated from the main sample of the experimental group used for formal gradient pressure testing, ensuring that the exploratory pressurization operation does not interfere with the state of the main sample. The mass of the independent subsample is usually weighed according to the minimum total mass required to completely cover all preset test nodes within the detection interval. The basic pressure increment refers to the fixed pressure step size between adjacent test nodes within the detection interval. The measuring device takes the starting test node as the first test node and sequentially adds the basic pressure increment to determine the pressure value of each subsequent test node until the end detection boundary is reached or approached, thereby uniformly distributing all nodes of the discrete test pressure sequence within the detection interval.

[0089] After the measuring equipment completes the calculation of the boundary parameters of the detection interval and writes the starting test node and the ending detection boundary into the data cache, the process enters the probing pressurization test stage based on independent subsamples. The core task of this stage is to collect the gas production response data at each test node by performing standardized gradient pressurization operations on independent subsamples within the detection interval. This provides a verification basis based on the measured data of the current batch for the final confirmation of the formal discrete test pressure sequence. At the same time, all covered test nodes within the detection interval are extracted and output as discrete test pressure sequences for use in step S205.

[0090] Specifically, the measuring device reads the starting test node, the ending detection boundary, and the basic pressure increment parameters from the data cache, calculates the pressure value list of all test nodes within the detection interval, and then schedules the pressurization device to perform pressurization operations on the independent sub-samples corresponding to each test node in the independent sub-samples in sequence. The pressurization operation of each independent sub-sample follows the same pressurization, pressure holding, and demolding process specifications as the main sample of the experimental group in step S205. After demolding is completed, the powder is transferred and packaged, and the gas generation module collects the gas generation data. The measuring device receives gas production response data of independent sub-samples at each test node in real time. It performs gradient response analysis on the gas production data of all nodes to identify whether the expected nonlinear surge characteristic appears in the detection interval as the gas production increases with pressure. If the nonlinear surge characteristic is successfully identified in the detection interval, it confirms that the detection interval effectively covers the turning pressure of the target batch of powder. The pressure value list of all tested nodes in the detection interval is extracted and output as a discrete test pressure sequence. If the nonlinear surge characteristic is not identified in the detection interval, the measuring device should determine whether the surge point falls outside the boundary of the detection interval. If it is determined that the surge point may be located above the termination detection boundary, it automatically extends the termination detection boundary in the high pressure direction by a preset supplementary extension amount and performs additional pressure tests on the independent sub-samples of the newly added test nodes in the extended area until the nonlinear surge characteristic is captured before outputting a complete discrete test pressure sequence.

[0091] In some embodiments, pressurization testing based on independent subsamples within the detection interval and outputting a discrete test pressure sequence can be achieved in various ways: Optionally, the measuring device uses the starting test node as the first pressure value, and calculates the pressure values ​​of each subsequent node sequentially with a fixed step size of the base pressure increment until the termination detection boundary is reached. An ordered list of all node pressure values ​​is used as the test node scheduling sequence. Subsequently, a corresponding number of independent subsamples are weighed from the independent subsamples in sequence, and the pressurization device is controlled to apply the corresponding node pressure value to each independent subsample sequentially and maintain the pressure for a preset holding time. After demolding and transfer packaging, the gas generation module collects the gas generation data of each node, and performs adjacent node difference quotient calculation on the gas generation data of all nodes. The position of the node that obtains the maximum value in the difference quotient sequence is used to confirm whether the nonlinear surge point falls within the range. Within the detection range, if the pressure value of all covered nodes is confirmed to fall within the range, the pressure values ​​of all covered nodes are output as a discrete test pressure sequence. Optionally, the measuring device adopts an adaptive step-size detection strategy, sparsely arranging a small number of coarse-grained detection nodes within the detection range with an initial step size of twice the base pressure increment. Coarse-grained pressurization tests are performed on independent subsamples and gas production data is collected. Through difference quotient analysis, the nonlinear surge range is initially located to the sub-range between adjacent coarse-grained nodes. Subsequently, fine-grained nodes are arranged in this sub-range with a standard base pressure increment, and fine-grained pressurization tests are performed on independent subsamples. The complete list of node pressure values ​​after merging the coarse-grained nodes and the fine-grained supplementary nodes is output as a discrete test pressure sequence. This method reduces the consumption of independent subsamples in non-critical pressure segments while ensuring the accuracy of the transition range characterization. It is understandable that a node advancement strategy based on real-time monitoring of online gas production rate can also be adopted. After each independent subsample completes the pressurization and demolding sealing operation of a node, the gas production module outputs the instantaneous gas production rate of the current node in real time. The measuring device determines the pressure value of the next test node based on the changing trend of the instantaneous gas production rate. If the gas production rate of the current node is higher than that of the previous node, the pressure gap between the next node and the current node is reduced to increase the density of detection. If the increase in gas production rate is stable, the standard base pressure increment is maintained to advance, thereby realizing the adaptive adjustment of the detection node density with the change of gas production response. This is not limited here.

[0092] In some embodiments, before controlling the independent sub-samples pre-divided from the experimental group silicon-based powder samples to perform a pressure test, the measuring device acquires the material process identifier corresponding to the experimental group silicon-based powder samples, matches the material process identifier with the preset coating modification flag, and when it is determined that the material process identifier matches the coating modification flag, the amount of gas production volume change collected by the independent sub-samples in the pressure test is corrected to obtain the gradient response parameters after eliminating the interference of pseudo-signals. Specifically, the measuring device extracts the material process identification field from the material information record of the target batch of powder. This field records whether the silicon-based powder has undergone surface coating modification treatment during the synthesis stage. The coating modification treatment type includes at least one of carbonaceous coating, oxide coating, or polymer coating. The measuring device matches the extracted material process identification with the system's preset coating modification flag. The coating modification flag is a preset identification code that characterizes the presence of a heterogeneous coating layer on the surface of the silicon-based powder. If the material process identification and the coating modification flag are successfully matched, it is determined that a heterogeneous coating layer exists on the surface of the silicon-based powder sample in the current experimental group. The gas generation data collected in the subsequent independent sub-sample pressure test may be mixed with short-term pulse-type gas generation pseudo-signals caused by coating layer damage. It is necessary to correct the gas generation volume change before using it for gradient response parameter calculation to avoid interference from coating layer pseudo-signals on the determination of nonlinear surge point location.

[0093] In some embodiments, the specific process by which the measuring device corrects the gas production volume change data collected by the measuring device during the pressurization test of an independent sub-sample to obtain the gradient response parameters after eliminating spurious signal interference is as follows: The measuring device acquires the instantaneous gas production rate data continuously collected within a preset sampling window period after the independent sub-sample has been pressurized at the current test node, and generates a reaction rate time-domain waveform; it determines the amplitude difference between the peak value of the reaction rate time-domain waveform and the steady-state gas production rate baseline, where the steady-state gas production rate baseline is the average rate value corresponding to the gas production rate stabilizing at the end of the preset sampling window period; it takes the time corresponding to when the gas production rate in the reaction rate time-domain waveform first reaches a preset proportion of the difference between the steady-state gas production rate baseline and the peak value as the start timestamp, and the time corresponding to when the gas production rate falls back to the steady-state gas production rate baseline after decreasing as the end timestamp, and determines the time difference between the end timestamp and the start timestamp as the reaction convergence time constant. After obtaining the reaction convergence time constant, the measuring device compares it with a preset peeling threshold, which is a preset threshold characterizing the upper limit of the gas production convergence time constant caused by passivation shell cracking. If the reaction convergence time constant is less than the stripping threshold, the measuring device determines that the current gas generation is a short-term pulse type. This gas generation mode corresponds to the physical process in which the surface chemical energy released by the local cracking of the coating layer or passivation shell during the instant of pressure is rapidly converted into gas. This process is short in duration and the gas generation is concentrated. It differs in time domain characteristics from the gas generation caused by the continuous chemical corrosion caused by the deep fracture of the core silicon material. The measuring device multiplies the gas generation volume change by a preset suppression coefficient to generate a reduced characterization value. The suppression coefficient is a preset constant less than 1, which is used to suppress the influence weight of the coating layer pseudo signal on the subsequent gradient response parameter calculation. If the reaction convergence time constant is not less than the stripping threshold, the measuring device determines that the current gas generation is a continuous gas generation caused by the fracture of the deep core structure. This gas generation mode corresponds to the physical process in which the silicon particles undergo deep fracture after pressure, and a large amount of fresh silicon surface is continuously exposed and reacts with the solvent. This process is long in duration and the gas generation rate slowly decays after the initial peak. It is an effective gas generation signal that truly reflects the pressure resistance and crushing degree of silicon-based powder. The measuring device keeps the gas generation volume change as a non-destructive characterization value. After determining the reduced or non-destructive characterization value, the measuring device determines the ratio of the corresponding characterization value to the discrete test pressure increment applied at the test node as the gradient response parameter. This parameter characterizes the net effective gas production generated by independent subsamples under a unit pressure increment after pseudo-signal correction. It is the core input data for subsequent nonlinear surge point determination and final confirmation of discrete test pressure sequence.

[0094] S205. After performing a simulation operation of a preset processing procedure based on a discrete test pressure sequence on a silicon-based powder sample in the control experimental group, determine the total gas production volume sequence under the discrete test pressure sequence.

[0095] S206. Determine the net gas production parameter sequence based on the difference between the total gas production volume sequence and the gas production baseline.

[0096] S207. The net gas production parameter sequence is converted based on the chemical reaction conversion coefficient corresponding to the preset solvent to obtain the silicon mass participating in the reaction under each discrete test pressure, and the ratio of the silicon mass participating in the reaction to the initial mass is determined as the degree of fragmentation under the corresponding discrete test pressure.

[0097] Steps S205~S207 and Figure 2 Steps S102 to S104 in the illustrated embodiment are similar and can be referred to. Figure 2 The descriptions in steps S102 to S104 will not be repeated here.

[0098] It is understandable that steps S202 to S204, by integrating the adaptive narrowing mechanism of the detection range based on the historical benchmark critical pressure of the target supplier with the independent sub-sample probing calibration process, achieve the focus of the node distribution of the discrete test pressure sequence from the entire pressure domain to the key pressure range where the target batch of powder is most likely to exhibit large-scale fragmentation behavior. This improves the characterization density of the pressure resistance characteristic curve and the positioning accuracy of the turning pressure value within the turning range with limited sample consumption and testing time. In some embodiments, if the number of historical batch records corresponding to the target batch of silicon-based powder is sufficient, the variance of the historical critical pressure distribution is extremely small, and the current batch process parameters are completely consistent with those of the historical batches, steps S202 to S204 may not be executed. In this case, after executing step S201, step S205 can be executed directly, and the measuring equipment directly constructs the discrete test pressure sequence with the historical benchmark critical pressure as the center and a preset uniform step size across the entire domain, skipping the probing calibration step based on independent sub-samples. This is not limited here.

[0099] In this embodiment, a fusion mechanism is adopted that retrieves historical benchmark critical pressure based on the target supplier identification code, constructs an adaptive detection interval using the historical benchmark critical pressure as an anchor point through safe sinking offset and safe buoyancy offset, and verifies the discrete test pressure sequence through independent sub-sample probing pressurization tests within the detection interval. This allows the range and node distribution of the discrete test pressure sequence to be calibrated based on historical prior information and further combined with the measured gas production response characteristics of the current batch of powder. This effectively solves the technical problems in related technologies, such as the inefficient consumption of test resources in non-critical pressure sections due to uniformly arranging test nodes across the entire pressure domain, and the limited fitting accuracy of the breakage response function due to insufficient node density in critical transition intervals. Thus, it achieves the technical effect of improving the positioning accuracy of the transition pressure value and the reliability of the pressure resistance performance determination result while reducing the sample consumption required for a single test and shortening the total test cycle.

[0100] The exemplary measuring device 50 provided in the embodiments of this application is described below. Figure 4 This is an exemplary hardware structure diagram of the measuring device 50 provided in the embodiments of this application.

[0101] In some embodiments, the measuring device 50 is a computer device or includes a computer device. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0102] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0103] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0104] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0105] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0106] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for determining the pressure resistance of silicon-based powder, characterized in that, Applied to a measuring device, the method includes: The baseline of gas production from the reaction of the control group silicon-based powder sample in a preset solvent was received; After performing a simulated operation of a preset processing procedure based on a discrete test pressure sequence on the silicon-based powder sample of the experimental group, the total gas production volume sequence under the discrete test pressure sequence is determined. The initial mass of the silicon-based powder sample of the experimental group is the same as that of the silicon-based powder sample of the control group and they belong to the same physical batch. The net gas production parameter sequence is determined based on the difference between the total gas production volume sequence and the gas production baseline; The net gas production parameter sequence is converted based on the chemical reaction conversion coefficient corresponding to the preset solvent to obtain the silicon mass participating in the reaction under each discrete test pressure, and the ratio of the silicon mass participating in the reaction to the initial mass is determined as the degree of fragmentation under the corresponding discrete test pressure.

2. The method according to claim 1, characterized in that, After determining the ratio of the mass of silicon participating in the reaction to the initial mass as the degree of fragmentation under the corresponding discrete test pressure, the method further includes: Using each discrete test pressure in the discrete test pressure sequence as the independent variable and the corresponding fragmentation degree as the dependent variable, a fragmentation degree response function of the target batch of silicon-based powder is constructed by curve fitting. The target batch of silicon-based powder includes the control group silicon-based powder sample and the experimental group silicon-based powder sample. Performing a first-order derivative operation on the fragmentation response function yields a derivative function characterizing the rate of change of fragmentation with increasing pressure; Solve for the function value of the derivative function within the range of the independent variable, and mark the pressure coordinate point where the derivative function reaches its maximum value as the turning pressure value. The turning pressure value characterizes the pressure threshold corresponding to the large-scale fragmentation of the target batch of silicon-based powder. The degree of breakage corresponding to the turning pressure value and the turning pressure value are used as the pressure resistance performance judgment result of the target batch of silicon-based powder. The pressure resistance performance judgment result is used to make a horizontal comparison of the pressure resistance of silicon-based powders of different batches or different modification processes.

3. The method according to claim 2, characterized in that, After the step of using the breakage degree corresponding to the breakage pressure value and the breakage pressure value as the pressure resistance performance determination result of the target batch of silicon-based powder, the method further includes: Obtain the standard critical stress value corresponding to a historical batch of silicon-based powder under the same material formulation identifier as the target batch of silicon-based powder, and calculate the deviation of the turning pressure value of the target batch of silicon-based powder from the standard critical stress value. The deviation is the normalized ratio of the difference between the standard critical stress value and the turning pressure value to the standard critical stress value. If the deviation exceeds a preset deviation threshold, process compensation parameters for the target batch are generated based on the deviation through a preset deviation-process compensation mapping relationship.

4. The method according to claim 3, characterized in that, The step of generating process compensation parameters for the target batch based on the deviation through a preset deviation-process compensation mapping relationship specifically includes: The deviation is input into a pre-calibrated deviation-fragmentation-filling-pore ratio mapping table for interpolation calculation, and the dense filling rate corresponding to the deviation is output. The deviation-fragmentation-filling-pore ratio mapping table represents the mapping relationship between the deviation value and the proportion of the additional thickness reduction of the electrode sheet caused by particle breakage and filling of internal pores in the corresponding rolling process. The dense filling rate represents the proportion of non-true dense thickness in the actual thickness of the electrode sheet caused by particle breakage and filling of pores. Based on the standard electrode thickness qualification threshold corresponding to the current rolling process, the product of the standard electrode thickness qualification threshold and the dense filling rate is determined as the thickness compensation correction amount. The thickness compensation correction amount is subtracted from the standard electrode thickness qualification threshold to generate the electrode thickness qualification threshold, and the electrode thickness qualification threshold is used as the thickness qualification criterion for the target batch of silicon-based powder.

5. The method according to claim 1, characterized in that, Prior to the step of determining the total gas production volume sequence under the discrete test pressure sequence, the method further includes: Based on the target supplier identification code corresponding to the silicon-based powder sample of the experimental group, the historical benchmark critical pressure matching the target supplier identification code is retrieved from the historical supply database. The historical benchmark critical pressure is the statistical median of the pressure corresponding to the nonlinear surge in gas production when the same batch of silicon powder with the same identification code is subjected to the same simulation test. The starting test node of the discrete test pressure sequence is obtained by subtracting the preset safe sink offset from the historical benchmark critical pressure. The termination detection boundary of the discrete test pressure sequence is obtained by superimposing the preset safe buoyancy offset on the historical benchmark critical pressure. The safe buoyancy offset is a detection margin preset based on the variance of the historical batch pressure distribution. Within the detection interval formed by the starting test node and the ending detection boundary, the independent sub-samples pre-divided from the silicon-based powder samples in the experimental group are controlled to perform pressure tests with a preset base pressure increment. The independent sub-samples are independent subsets used to perform exploratory calibration of the range and step size distribution of the discrete test pressure sequence. All covered test nodes within the detection interval are extracted and output as the discrete test pressure sequence.

6. The method according to claim 5, characterized in that, Prior to the step of controlling the pressure test to be performed on pre-divided independent sub-samples from the experimental group of silicon-based powder samples at a preset base pressure increment, the method further includes: Obtain the material process identifier corresponding to the silicon-based powder sample of the experimental group. The material process identifier is an attribute field that records whether the silicon-based powder has undergone surface coating modification treatment during the synthesis stage. The material process identifier is matched with a preset coating modification flag, which is a preset identification code that characterizes the presence of a heterogeneous coating layer on the surface of silicon-based powder. If the material process identifier matches the coating modification flag, the gas production volume change collected by the independent sub-sample in the pressurization test is corrected to obtain the gradient response parameters after eliminating spurious signal interference.

7. The method according to claim 6, characterized in that, The step of correcting the gas production volume change data collected by the independent sub-samples during the pressurization test to obtain the gradient response parameters after eliminating spurious signal interference specifically includes: The instantaneous gas production rate data of the independent sub-samples are continuously collected within a preset sampling window period after the current test node is fully pressurized, and the reaction rate time domain waveform is generated. Determine the amplitude difference between the peak value of the reaction rate time-domain waveform and the steady-state gas production rate baseline, wherein the steady-state gas production rate baseline is the average rate value corresponding to the gas production rate stabilizing at the end of the preset sampling window period; The time when the gas production rate in the reaction rate time domain waveform first reaches a preset ratio of the difference between the steady-state gas production rate baseline and the peak value is taken as the start time stamp, and the time when the gas production rate drops back to the steady-state gas production rate baseline is taken as the end time stamp. The time difference between the end time stamp and the start time stamp is determined as the reaction convergence time constant. The reaction convergence time constant is compared with a preset stripping threshold to obtain the corresponding characterization value. The stripping threshold is a preset threshold that characterizes the upper limit of the gas production convergence time constant caused by passivation shell cracking. The ratio of the characterization value to the corresponding discrete test pressure increment is determined as the gradient response parameter.

8. A measuring device, characterized in that, The measuring device includes: one or more processors and at least one memory; any one or more of the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the measuring device to perform the method as described in any one of claims 1-7.

9. A computer program product containing instructions, characterized in that, When the computer program product is run on the measuring device, the measuring device performs the method as described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the measuring device, the measuring device performs the method as described in any one of claims 1-7.

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