Method and device for determining oil guiding rate of liquid storage cotton, electronic equipment and program product

Abstract

This disclosure belongs to the field of electronic heater technology and provides a method and apparatus for determining the oil conduction rate of a liquid storage cotton. The method involves acquiring seepage evolution data of the liquid storage cotton during the seepage process. The seepage process characterizes the longitudinal seepage of the target liquid along the liquid storage cotton due to capillary driving force, and the seepage evolution data characterizes the corresponding evolution relationship between the wetting length of the liquid storage cotton under the action of capillary driving force and the acquisition time. Based on a capillary seepage oil conduction rate determination model, the instantaneous oil conduction rate of the liquid storage cotton is determined according to the seepage evolution data. The capillary seepage oil conduction rate determination model characterizes the physical correlation between the wetting length, wetting time, and instantaneous oil conduction rate, and is obtained through product coupling dynamic correction of dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor. Therefore, the instantaneous oil conduction rate of the liquid storage cotton can be detected in a timely manner, avoiding delayed or misjudged assessments of the risk of dry burning.

Description

Technical Field

[0001] This disclosure pertains to the field of testing electronic heaters, and more specifically, relates to a method, apparatus, electronic equipment, and program product for determining the oil conduction rate of a liquid storage cotton. Background Technology

[0002] The electronic heater can be equipped with a heating component, which includes a liquid storage cotton. The liquid storage cotton can guide the target liquid longitudinally to the heating element by capillary force. In the working scenario of the electric heater, the target liquid can exhibit non-Newtonian rheological properties, and the porous channels of the liquid storage cotton can dynamically contract due to liquid absorption and swelling. All of these factors can cause the oil conduction rate of the liquid storage cotton to change dynamically.

[0003] Classical capillary flow theory equates porous media to rigid, straight capillaries of constant diameter and assumes constant fluid properties. The oil conduction rate determined by this theory deviates significantly from actual operating conditions, making it impossible to accurately determine the instantaneous oil conduction rate of the reservoir cotton. Consequently, traditional electronic heaters struggle to detect the instantaneous oil conduction rate of the reservoir cotton in a timely manner, failing to obtain real-time information on the actual oil conduction capacity of the reservoir cotton to the heating element. This leads to delayed or misjudged assessments of the risk of dry burning. Summary of the Invention

[0004] The purpose of this disclosure is to provide a method, apparatus, electronic device, and program product for determining the oil conduction rate of a liquid storage cotton, aiming to solve the technical problem that the prior art cannot accurately determine the instantaneous oil conduction rate of the liquid storage cotton, resulting in a delayed or misjudged assessment of the risk of dry burning of the electronic heater.

[0005] To achieve the above objectives, in a first aspect, embodiments of this disclosure provide a method for determining the oil conduction rate of a liquid storage cotton, the method comprising: The seepage evolution data of the liquid storage cotton during the seepage process is obtained. The seepage process is used to characterize the longitudinal seepage of the target liquid along the liquid storage cotton due to capillary driving force. The seepage evolution data is used to characterize the corresponding evolution relationship between the wetting length of the liquid storage cotton under the action of capillary driving force and the collection time. Based on the capillary seepage oil conduction rate determination model, the instantaneous oil conduction rate of the liquid storage cotton is determined according to the seepage evolution data; The capillary flow oil conduction rate determination model is used to characterize the physical relationship between immersion length, immersion time, and instantaneous oil conduction rate, and is obtained by product coupling dynamic correction of dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor. The dynamic effective pore size is used to characterize the effective pore size of the porous channels of the liquid storage cotton after it absorbs and swells with the target liquid. The composite dynamic viscosity is used to characterize the apparent viscosity of the target liquid as a function of shear rate and temperature. The dynamic surface tension is used to characterize the thermal degradation characteristics of the target liquid. The tortuosity factor is used to characterize the damping effect of the path tortuosity of the porous channels of the liquid storage cotton on the driving pressure difference.

[0006] Secondly, embodiments of this disclosure provide an apparatus for determining the oil conduction rate of a liquid storage cotton, the apparatus comprising: A data acquisition component is used to acquire the wetting length of the liquid storage cotton during the seepage process, and the seepage process is used to characterize the longitudinal seepage of the target liquid along the liquid storage cotton due to capillary driving force. The processor, connected to the data acquisition component, is used to acquire seepage evolution data of the liquid storage cotton during the seepage process. The seepage evolution data is used to characterize the evolution relationship between the immersion length of the liquid storage cotton under the action of capillary driving force and the acquisition time. Based on the capillary seepage oil conduction rate determination model, the instantaneous oil conduction rate of the liquid storage cotton is determined according to the seepage evolution data. The capillary flow oil conduction rate determination model is used to characterize the physical relationship between immersion length, immersion time, and instantaneous oil conduction rate, and is obtained by product coupling dynamic correction of dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor. The dynamic effective pore size is used to characterize the effective pore size of the porous channels of the liquid storage cotton after it absorbs and swells with the target liquid. The composite dynamic viscosity is used to characterize the apparent viscosity of the target liquid as a function of shear rate and temperature. The dynamic surface tension is used to characterize the thermal degradation characteristics of the target liquid. The tortuosity factor is used to characterize the damping effect of the path tortuosity of the porous channels of the liquid storage cotton on the driving pressure difference.

[0007] Thirdly, embodiments of this disclosure provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electronic device performs the method as described in any of the first aspects.

[0008] Fourthly, embodiments of this disclosure provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described in any of the first aspects.

[0009] Fifthly, embodiments of this disclosure provide a computer program product that, when run on an electronic device, causes the electronic device to perform the method described in any one of the first aspects.

[0010] The beneficial effects of the embodiments disclosed herein compared to the prior art are as follows: This disclosure provides a method for determining the oil conduction rate of a liquid-absorbing cotton. By acquiring seepage evolution data of the liquid-absorbing cotton during the seepage process, and based on a capillary seepage oil conduction rate determination model, the instantaneous oil conduction rate of the liquid-absorbing cotton is determined according to the seepage evolution data. The seepage process characterizes the longitudinal seepage of the target liquid along the liquid-absorbing cotton due to capillary driving force; the seepage evolution data characterizes the corresponding evolution relationship between the wetting length of the liquid-absorbing cotton under the action of capillary driving force and the acquisition time; the capillary seepage oil conduction rate determination model characterizes the wetting length, wetting time, and instantaneous oil conduction rate. The physical relationship between oil conduction rates was obtained by dynamically correcting the dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor through product coupling. The dynamic effective pore size characterizes the effective pore size of the porous channels of the liquid storage cotton after it absorbs and swells with the target liquid. The composite dynamic viscosity characterizes the apparent viscosity of the target liquid as a function of shear rate and temperature. The dynamic surface tension characterizes the thermal degradation characteristics of the target liquid. The tortuosity factor characterizes the damping effect of the path tortuosity of the porous channels of the liquid storage cotton on the driving pressure difference.

[0011] This method acquires the seepage evolution data of the liquid storage cotton during the seepage process, and determines the instantaneous oil conduction rate based on the seepage evolution data using a capillary seepage oil conduction rate determination model. The capillary seepage oil conduction rate determination model performs product coupling dynamic correction on the dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor. It can accurately determine the instantaneous oil conduction rate of the liquid storage cotton based on the seepage evolution data. The constant parameter assumptions that cannot be directly applied in the classical capillary seepage theory are replaced with dynamic correction factors that are consistent with the actual working conditions, so that the determination result of the instantaneous oil conduction rate can reflect the real flow state of non-Newtonian, non-isothermal, porous and multiphase flow. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating the method for determining the oil conduction rate of the liquid storage cotton provided in this embodiment of the present disclosure; Figure 2This is a flowchart illustrating the method for determining the oil conduction rate of the liquid storage cotton provided in this embodiment of the present disclosure; Figure 3 This is a flowchart illustrating the method for determining the oil conduction rate of the liquid storage cotton provided in this embodiment of the present disclosure; Figure 4 This is a flowchart illustrating the method for determining the oil conduction rate of the liquid storage cotton provided in this embodiment of the present disclosure; Figure 5 This is a schematic flowchart of the oil conduction rate determination device for liquid storage cotton provided in the embodiments of this disclosure; Figure 6 This is a schematic diagram of the structure of the oil conduction rate determination device for the liquid storage cotton provided in the embodiments of this disclosure; Figure 7 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this disclosure. Detailed Implementation

[0014] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, so as to provide a thorough understanding of the embodiments of this disclosure. However, those skilled in the art will understand that this disclosure may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this disclosure with unnecessary detail.

[0015] It should be understood that, when used in this disclosure and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0016] It should also be understood that, in the description of this disclosure, unless otherwise stated, the " / " used in this specification and the appended claims indicates that the related objects are in an "or" relationship. For example, A / B can mean A or B. The "and / or" in this disclosure is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. Furthermore, in the description of this disclosure, unless otherwise stated, "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0017] Furthermore, to facilitate a clear description of the technical solutions of the embodiments of this disclosure, the terms "first" and "second" are used in the embodiments of this disclosure to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, but are only used for distinguishing descriptions, and the terms "first" and "second" are not necessarily different, nor should they be construed as indicating or implying relative importance.

[0018] As used in this disclosure and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [the described condition or event] is detected," or "in response to detection of [the described condition or event]."

[0019] References to "one embodiment" or "some embodiments" as described in this disclosure mean that one or more embodiments of this disclosure include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless otherwise specifically emphasized.

[0020] This disclosure provides an example of a method for determining the oil conduction rate of a liquid storage cotton. Please refer to... Figure 1 As shown, Figure 1 A schematic flowchart of a method for determining the oil conduction rate of a liquid storage cotton provided in this disclosure is shown. This is an example and not a limitation; the method can be applied to or operated in a device for determining the oil conduction rate of a liquid storage cotton. The method includes: S101. Obtain seepage evolution data of the liquid storage cotton during the seepage process.

[0021] Among them, the seepage process is used to characterize the longitudinal seepage of the target liquid along the storage cotton due to capillary driving force, and the seepage evolution data is used to characterize the corresponding evolution relationship between the wetting length of the storage cotton under the action of capillary driving force and the collection time.

[0022] S102. Based on the capillary seepage oil conduction rate determination model, determine the instantaneous oil conduction rate of the liquid storage cotton according to the seepage evolution data.

[0023] Among them, the capillary flow oil conduction rate determination model is used to characterize the physical relationship between immersion length, immersion time and instantaneous oil conduction rate, and is obtained by product coupling dynamic correction of dynamic effective pore size, composite dynamic viscosity, dynamic surface tension and tortuosity factor; dynamic effective pore size is used to characterize the effective pore size of the porous channels of the liquid storage cotton after it absorbs the target liquid and swells; composite dynamic viscosity is used to characterize the apparent viscosity of the target liquid as a function of shear rate and temperature; dynamic surface tension is used to characterize the thermal degradation characteristics of the target liquid; tortuosity factor is used to characterize the damping effect of the path tortuosity of the porous channels of the liquid storage cotton on the driving pressure difference.

[0024] In this embodiment, the liquid storage cotton can be the oil-wicking cotton in an electronic heater, specifically, but not limited to, polyethylene terephthalate and polyamide 6 integrated cotton, with a density of 0.085 g / cm³ and a porosity of 95%. The target liquid can be the liquid to be heated, for example, but not limited to, a glycerol-propylene glycol mixture with a density of 1.104 g / cm³ and a viscosity of 130.4 mPa·s (25°C).

[0025] In this embodiment, the capillary driving force is the pressure difference generated by surface tension in the porous channels of the storage cotton for the target liquid. The magnitude of this driving force is determined at least by the dynamic surface tension of the target liquid and the geometric characteristics of the porous channels of the storage cotton. Its physical expression can be that the capillary pressure difference is equal to the product of four times the dynamic surface tension and the cosine of the contact angle divided by the effective pore size of the porous channels. Longitudinal percolation represents the one-dimensional propagation of the target liquid along the length of the storage cotton, and lateral diffusion can be ignored in this process.

[0026] In step S101, seepage evolution data during the seepage process is acquired. This seepage evolution data is a set of data characterizing the evolution relationship between the wetting length of the storage cotton under capillary driving force and the time of data collection. The purpose of this seepage evolution data is to transform the complex multiphase seepage process inside the storage cotton, which cannot be directly observed, into a spatiotemporal discrete sequence that can be processed by computer algorithms. This provides the basic input for determining the instantaneous oil conduction rate. Through the collaboration of hardware acquisition equipment and software processing modules, the oil conduction rate, which was originally impossible to measure directly, can be indirectly calculated through the relationship between the wetting length and the passage of time.

[0027] For example, seepage evolution data can be represented as a discrete key-value pair sequence arranged in order of acquisition time, where each key-value pair contains an acquisition time identifier and the corresponding wetting length at that time. Alternatively, seepage evolution data can also be represented as a function curve with acquisition time as the independent variable and wetting length as the dependent variable, which reflects the trajectory of the liquid front advancing in the liquid storage cotton.

[0028] For example, during the seepage process, a high-speed camera can be used to continuously acquire longitudinal images of the liquid-storing cotton at fixed sampling intervals, and the position of the liquid front can be identified from the images to obtain the wetting length corresponding to each sampling moment. Alternatively, a weighing sensor can be used to continuously acquire the liquid mass of the liquid-storing cotton at fixed sampling intervals, and the wetting length corresponding to each sampling moment can be obtained based on the conversion relationship between mass and volume. The sampling interval can be set to 0.5s. By acquiring seepage evolution data, the actual flow process can be quantified as the evolution relationship between wetting length and sampling moment, thus providing observational basis for model calculation without relying on hardware for directly measuring the oil conduction rate.

[0029] In step S102, the instantaneous oil conduction rate of the liquid storage cotton is determined based on the capillary seepage oil conduction rate determination model and the seepage evolution data. The capillary seepage oil conduction rate determination model is a mathematical model that characterizes the physical relationship between the wetting length, wetting time and instantaneous oil conduction rate, and is constructed by dynamically correcting the dynamic effective pore size, composite dynamic viscosity, dynamic surface tension and tortuosity factor through product coupling. This model can be stored in memory as computer-executable instructions and invoked by the processor when executing the method for determining the oil conduction rate of the liquid storage cotton. That is, the idealized constant assumptions in the classical Lucas-Washburn model are replaced with dynamic variables that conform to actual working conditions, so that the determination result of the instantaneous oil conduction rate can reflect the real flow state of non-Newtonian, non-isothermal, porous and multiphase flow. This reduces the error of the classical Lucas-Washburn model from 19.7% under normal temperature conditions and 37.2% under high temperature conditions to the range of 3.1% to 5.8% of this model. This determination process is a technical solution adopted to solve the technical problem of inaccurate oil conduction rate measurement under real working conditions.

[0030] For example, the wetting length corresponding to the current acquisition moment in the seepage evolution data can be substituted into the capillary seepage oil conduction rate determination model for numerical solution to obtain the instantaneous oil conduction rate at the current acquisition moment. Alternatively, the complete wetting length sequence from the seepage evolution data can be input into the model, and the processor can calculate and output the instantaneous oil conduction rate curve over time point by point. Furthermore, the model can be used to perform time-series recursion on the wetting length, predicting the wetting length at the next moment from the wetting length at the previous moment and the instantaneous oil conduction rate. By determining the instantaneous oil conduction rate based on the capillary seepage oil conduction rate determination model and seepage evolution data, the processor can output the instantaneous oil conduction rate at any moment without changing the existing testing fixtures and acquisition hardware, achieving precise quantification of the oil conduction rate.

[0031] In this embodiment, the dynamic effective pore size in the capillary flow oil conduction rate determination model is a parameter characterizing the effective pore size of the porous channels of the liquid storage cotton after it absorbs and swells from the target liquid. The role of the dynamic effective pore size is to correct the distortion in the classical model (i.e., the ideal straight pipe reference model, such as the classical Lucas-Washburn model) that treats the pore size as a rigid constant parameter. It reflects the physical fact that the effective pore size of the porous channels dynamically shrinks with saturation due to the microscopic expansion of the liquid storage cotton fibers after absorbing the target liquid. Together with the composite dynamic viscosity, dynamic surface tension, and tortuosity factor, it constitutes one of the four dimensions of product-coupled dynamic correction. This parameter can be obtained by the processor in real time based on the wetting length.

[0032] For example, the dynamic effective pore size can be expressed as the product of the initial effective radius and the swelling-shrinkage factor, that is, the dynamic effective pore size reff(L) is equal to the initial effective radius r0 multiplied by the swelling-shrinkage coefficient χ(L), where the swelling-shrinkage coefficient χ(L) is equal to 1 minus the product of the swelling-shrinkage coefficient ηs and the saturation ratio raised to the power of m, and the saturation ratio is equal to the wetting length L divided by the critical length for complete saturation Lmax, that is, reff(L) = r0·[1] The swelling and shrinkage coefficient ηs can be calibrated to a value ranging from 0.05 to 0.3, and the swelling nonlinearity index m can be calibrated to a value ranging from 1.0 to 2.0. By adopting a dynamic effective pore size, the model can describe the pore size reduction effect caused by fiber swelling in the mid-to-late stages of liquid storage cotton, avoiding the mid-to-late stage calculation distortion caused by the classical model treating the pore size as a rigid dead pore.

[0033] In this embodiment, composite dynamic viscosity is a parameter characterizing the apparent viscosity of the target liquid as a function of shear rate and temperature. The role of composite dynamic viscosity is to simultaneously incorporate the shear-thinning characteristics of the heated liquid under high shear rates in microporous channels and the viscosity decrease characteristics caused by temperature increases, thus correcting the assumption of constant viscosity in the classical model. Together with dynamic effective pore size, dynamic surface tension, and tortuosity factor, it constitutes one of the four dimensions of product-coupled dynamic correction. This parameter can be calculated in real time by the processor in conjunction with shear rate and local temperature.

[0034] For example, the composite dynamic viscosity can be expressed as a coupled form of power-law viscosity and temperature-dependent viscosity, that is, the composite dynamic viscosity ηdynamic is equal to the consistency coefficient K multiplied by the shear rate γ (n 1) Multiply by the power of the natural logarithm, with the exponent being B, and divide by (T). The power function of T∞), where n is the flow index (n < 1 indicates shear thinning), B is the viscosity-temperature dependent parameter, T is the local temperature, and T∞ is the divergence temperature, i.e., ηdynamic = K·γ^(n 1)·exp[B / (T [T∞)], where the flow index n can be a calibration value ranging from 0.6 to 0.95. By employing composite dynamic viscosity, it is possible to describe the time-varying characteristics of the viscosity of the heated liquid decreasing with increasing flow rate, as well as the thermal thinning characteristics of the viscosity index decreasing with increasing temperature.

[0035] In this embodiment, dynamic surface tension is a parameter characterizing the thermal degradation properties of the target liquid. The role of dynamic surface tension is to reflect the physical phenomenon of the decrease in surface tension of the target liquid as temperature increases. This characteristic also constitutes one of the sources of capillary driving force, allowing temperature field changes to be transmitted to the driving pressure difference via surface tension, thereby affecting the oil conduction rate. Together with dynamic effective pore size, composite dynamic viscosity, and tortuosity factor, it forms one of the four dimensions of product-coupled dynamic correction. This parameter can be calculated in real time by the processor based on the temperature field model.

[0036] For example, dynamic surface tension can be expressed as γ(T) = γ0·[1] δ·ΔT(t)·exp( λL), where γ0 is the reference surface tension, δ is the surface tension temperature coefficient, ΔT(t) is the near-end temperature rise of the heating element, λ is the temperature decay characteristic coefficient, and L is the wetting length. The surface tension temperature coefficient δ can be obtained through standard interface testing. By adopting dynamic surface tension, the model can incorporate the influence of the temperature gradient caused by dynamic heat transfer of the heating element on the surface tension, correcting the deficiency of classical models that cannot directly incorporate time-varying temperature fields.

[0037] In this embodiment, the tortuosity factor is a parameter characterizing the damping effect of the path tortuosity of the porous channel of the liquid storage cotton on the driving pressure difference. The role of the tortuosity factor is to correct the assumption in the classical model that the flow path is regarded as a straight pipe, reflecting the physical fact that the actual tortuosity of the porous path leads to a reduction in the effective driving pressure difference. Together with the dynamic effective pore size, composite dynamic viscosity, and dynamic surface tension, it constitutes one of the four dimensions of product-coupled dynamic correction. This parameter can be calculated by the processor based on local saturation.

[0038] For example, the tortuosity factor can be expressed as τ2(S)=τ2,0·[1] [α·S^β], where τ2,0 is the initial dry tortuosity damping factor, typically ranging from 0.62 to 0.85; α is the saturation sensitivity coefficient, typically ranging from 0.15 to 0.35; β is the nonlinear exponent, typically ranging from 1.5 to 2.5; and S is the local saturation, which can be approximated as the wetting length L divided by the critical length for complete saturation Lmax, i.e., S = L / Lmax. By employing the tortuosity factor, the model can describe the driving pressure differential damping effect caused by the tortuosity constraint on the actual fluid travel path.

[0039] As one implementation method, the ideal straight pipe reference model, namely the classic Lucas-Washburn model, is used as the theoretical framework. The dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor are substituted into the ideal straight pipe reference model in a product coupling manner to obtain the capillary seepage oil conduction rate determination model. The governing equations of the capillary seepage oil conduction rate determination model are reconstructed into the following governing equations: The instantaneous oil transfer rate V is equal to the derivative of the immersion length with respect to the acquisition time, dL / dt, and V = reff(L)·[γ(T)·cosθ·τ2(S) / (4·K·L·exp[B / (T)]). T∞)])]^(1 / n), where θ is the contact angle. The right side of this equation contains only the immersion length and acquisition time, as well as the physical property parameters obtained through calibration. All complex and unmeasurable variables are solved by the processor software through the immersion length and acquisition time, thereby realizing the total differential physical reconstruction of the classical Lucas-Washburn model without changing the existing hardware measurement system.

[0040] It should be noted that the above-mentioned capillary seepage oil conduction rate determination model and corresponding solution steps constitute a software algorithm module executed on a computer processor. This module uses seepage evolution data acquired by hardware acquisition equipment as algorithm input, and outputs the technical parameter of instantaneous oil conduction rate through multi-physics field coupling correction and differential equation solution. This process is a technical means adopted to solve the technical problem of excessive error of the classical Lucas-Washburn model under real working conditions. The oil conduction rate determination method of the liquid storage cotton constituted by this method is a patented technical solution.

[0041] This method acquires the seepage evolution data of the liquid storage cotton during the seepage process, and determines the instantaneous oil conduction rate based on the seepage evolution data using a capillary seepage oil conduction rate determination model. The capillary seepage oil conduction rate determination model performs product coupling dynamic correction on the dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor. It can accurately determine the instantaneous oil conduction rate of the liquid storage cotton based on the seepage evolution data. The constant parameter assumptions that cannot be directly applied in the classical capillary seepage theory are replaced with dynamic correction factors that are consistent with the actual working conditions, so that the determination result of the instantaneous oil conduction rate can reflect the real flow state of non-Newtonian, non-isothermal, porous and multiphase flow.

[0042] As one implementation method, please refer to Figure 2 As shown, Figure 2 A schematic flowchart of a method for determining the oil conduction rate of a liquid storage cotton provided in this disclosure is shown, which obtains seepage evolution data during the seepage process, including: S201. During the process of the target liquid seeping longitudinally along the storage cotton due to capillary driving force, acquire longitudinal images of the storage cotton collected by the data acquisition component at preset sampling intervals; and / or acquire the liquid mass of the storage cotton collected at preset sampling intervals. S202. Determine the immersion length corresponding to each acquisition time based on the longitudinal image and / or liquid mass; S203. Determine the seepage evolution data based on the infiltration length corresponding to each collection time.

[0043] As one implementation method, acquiring seepage evolution data during the seepage process can be understood as using hardware acquisition devices (data acquisition components) and software processing modules (processors) to collaboratively acquire the spatiotemporal discrete signal of the target liquid's propagation in the storage cotton, and converting this spatiotemporal discrete signal into a sequence corresponding to the immersion length and acquisition time. The preset sampling interval is a fixed time interval for the data acquisition component in the test fixture to acquire raw data, for example, it can be set to 0.5s, providing a unified time grid for the seepage evolution data, so that the subsequent single-point implicit iteration and time-series recursion based on the capillary seepage oil conduction rate determination model can be aligned with the hardware acquisition rhythm. This interval can also be used as the benchmark recursion step size mentioned above.

[0044] In some embodiments, the data acquisition component may include an imaging device and a weighing sensor. The longitudinal image is an image frame containing the liquid wetting front position, captured from the side of the liquid-retaining cotton using the imaging device (i.e., the data acquisition component), which may be a high-speed camera. The liquid mass is the total mass or change in mass of the liquid-retaining cotton and the adsorbed target liquid, measured using the weighing sensor, which may be a load cell.

[0045] For example, in the actual physical process where the liquid-absorbing cotton is clamped in the test fixture and the target liquid contacts one end and undergoes longitudinal seepage, a high-speed camera continuously captures image frames of the side of the liquid-absorbing cotton at a preset sampling interval, or a weighing sensor continuously measures the mass reading of the liquid-absorbing cotton at a preset sampling interval, or both acquisition paths are used simultaneously. This transforms the internal seepage process, which cannot be directly observed, into an externally measurable signal that can be processed by a processor, solving the technical problem of the lack of continuous observation methods in traditional oil conduction rate measurement. For example, a high-speed camera can be used to capture longitudinal images of the liquid-absorbing cotton at a preset sampling interval of 0.5 s. In the image, the target liquid-wetted area and the dry area form a clear grayscale or color boundary, and this boundary position is the liquid front. For example, a weighing sensor can be used to collect the total mass of the liquid-absorbing cotton and the adsorbed liquid at a preset sampling interval of 0.5 s, and the liquid mass increment at each moment can be obtained by subtracting it from the initial dry mass. For example, a high-speed camera and a weighing sensor can also be turned on simultaneously, acquiring longitudinal images and liquid mass at the same preset sampling interval, forming a dual-modal raw dataset for subsequent cross-validation.

[0046] In steps S201 to S202, the processor acquires longitudinal images of the liquid-storing cotton collected by the data acquisition component at preset sampling intervals; and / or acquires the liquid mass of the liquid-storing cotton collected at preset sampling intervals. It can also call image processing algorithms and / or mass conversion algorithms to convert the aforementioned acquired raw signals into immersion length values ​​corresponding to each acquisition time, unifying heterogeneous hardware sensing data into standardized geometric parameters. For example, for longitudinal images, the processor can perform edge detection or thresholding to identify the pixel coordinates of the liquid front edge, and then convert the pixel coordinates into immersion length based on the calibrated pixel physical ratio, i.e., the immersion length equals the pixel position of the liquid front edge multiplied by the physical size of a single pixel. For example, for liquid mass, the processor can divide the mass increment by the target liquid density to obtain the liquid volume, and then divide the liquid volume by the equivalent cross-sectional area of ​​the liquid-storing cotton to obtain the immersion length, i.e., the immersion length equals the liquid mass increment divided by the product of the target liquid density and the cross-sectional area of ​​the liquid-storing cotton, where the target liquid density can be 1.104 g / cm³. For example, for bimodal data, the processor can extract the immersion length from the longitudinal image and calculate the immersion length from the liquid mass, and output the immersion length corresponding to each acquisition time after performing a weighted average or consistency check on the two.

[0047] In S203, the processor organizes each acquisition time identifier and its corresponding immersion length value into an ordered data structure, thereby generating a dataset representing the evolution relationship between immersion length and acquisition time. For example, the immersion lengths corresponding to each acquisition time can be organized into a time-sorted sequence of key-value pairs, where each key-value pair contains an acquisition time identifier and the corresponding immersion length value; this sequence represents the seepage evolution data. Alternatively, each acquisition time and immersion length can be fitted into a discrete function table with the acquisition time as the independent variable and the immersion length as the dependent variable; this function table reflects the propagation trajectory of the liquid front in the storage cotton. For example, preset sampling interval metadata can also be recorded simultaneously when organizing the seepage evolution data, so that subsequent time-series recursion steps can directly read this interval as the baseline recursion step size.

[0048] By acquiring longitudinal images and / or liquid mass of the storage cotton collected by the test fixture at preset sampling intervals, and determining the wetting length corresponding to each acquisition time based on the longitudinal images and / or liquid mass to form seepage evolution data, the hardware acquisition capability of the test fixture can be combined with software algorithm processing to obtain the corresponding evolution sequence of wetting length and acquisition time required for the capillary seepage oil conduction rate determination model without adding temperature sensors or pore probes. This provides aligned time grids and geometric inputs for subsequent single-point implicit iterative solution of instantaneous oil conduction rate and step-size adaptive time-series recursion.

[0049] As one implementation method, please refer to Figure 3 As shown, Figure 3 A schematic flowchart of a method for determining the oil conduction rate of a liquid storage cotton provided in this disclosure is shown. Based on a capillary seepage oil conduction rate determination model, the instantaneous oil conduction rate of the liquid storage cotton is determined according to seepage evolution data, including: S301. Determine the current infiltration length corresponding to the current acquisition time from the seepage evolution data; S302. Substitute the current immersion length into the capillary flow oil conduction rate determination model, with the instantaneous oil conduction rate as the unknown quantity and the determined instantaneous oil conduction rate at the previous acquisition time as the initial value. Adaptively update the instantaneous oil conduction rate through the fixed-point iteration method or the Newton iteration method until the relative deviation value of two adjacent iterations is less than the preset deviation threshold.

[0050] Among them, the capillary flow oil conduction rate determination model correlates dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, tortuosity factor and instantaneous oil conduction rate in a product coupling manner.

[0051] In this implementation, the current acquisition time is the current solution time point reached by the processor at a fixed sampling interval during the acquisition of seepage evolution data. The current wetting length is the wetting length value in the seepage evolution data corresponding to the current acquisition time, which serves as the geometric input parameter for the capillary seepage conduction rate determination model. The previous acquisition time is the solution time point preceding the current acquisition time, and the instantaneous conduction rate at the previous acquisition time is the instantaneous conduction rate value obtained by the convergence of the capillary seepage conduction rate determination model in the previous solution cycle. This value can be used as the initial estimate for the current iteration.

[0052] For example, the fixed-point iteration method is a numerical method for iteratively solving implicit equations. It obtains the next iteration value by substituting the current iteration value into the right side of the equation, and repeats this process until convergence. Newton's iteration method is another numerical method for iteratively solving implicit equations. It approximates the roots of the equation by constructing a tangent line and has a second-order convergence rate.

[0053] For example, a preset deviation threshold is a critical value for determining iterative convergence, used to measure whether the relative deviation between two adjacent iterations meets the accuracy requirements. This threshold can be set to 10. -4 The calibration value of the order of magnitude. The capillary flow oil conduction rate determination model correlates the dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, tortuosity factor, and instantaneous oil conduction rate in a product coupling manner. Its implicit expression can be written as the instantaneous oil conduction rate V equals reff(L)·γ(T)·cosθ·τ2(S) divided by [4·K·(V / reff(L))^(n 1)·exp(B / (T In the implicit expression T∞))·L], the instantaneous oil conduction rate appears on both sides of the equation, so it is necessary to use an iterative method to solve it.

[0054] The current wetting length corresponding to the current acquisition time is determined from the seepage evolution data. That is, the processor reads the seepage evolution data sequence, extracts the wetting length value corresponding to the current solution time point, and provides the geometric boundary input of the current time for the capillary seepage oil conduction rate determination model, so that the model can solve for the instantaneous oil conduction rate at this specific time.

[0055] For example, a key-value pair matching the current acquisition time can be found in the seepage evolution data by indexing the acquisition time, and the infiltration length value in that key-value pair can be directly read as the current infiltration length. Alternatively, if the current acquisition time is not explicitly stored in the seepage evolution data, linear interpolation can be performed using the infiltration length values ​​of two adjacent acquisition times to obtain the current infiltration length corresponding to the current acquisition time. Furthermore, the infiltration length sequence in the seepage evolution data can be smoothed and filtered, and the filtered sequence can be used to determine the current infiltration length to reduce the interference of data noise on the iterative solution.

[0056] Subsequently, the processor inputs the current immersion length into the implicit equation of the capillary flow oil conduction rate determination model, using the instantaneous oil conduction rate as the unknown to be solved. The instantaneous oil conduction rate that has converged at the previous acquisition time is used as the initial value for iteration. The estimated value of the instantaneous oil conduction rate is repeatedly updated using the fixed-point iteration method or the Newton iteration method. After each iteration, the relative deviation between the results of two adjacent iterations is calculated. When the relative deviation is less than the preset deviation threshold, the iteration stops and the converged instantaneous oil conduction rate is output. The unknowns in the implicit equation that cannot be directly expressed analytically are solved by numerical calculation, which solves the technical problem of the difficulty in analytical solution caused by non-Newtonian, non-isothermal, and porous multiphase coupling in the capillary flow oil conduction rate determination model. This solution process is executed by the processor and constitutes the algorithm link in the technical solution. Since the temperature, saturation, and dynamic effective pore size are all determined only by the immersion length, no temperature sensor or pore probe is required during the iteration process.

[0057] For example, the implicit equation can be rearranged into V using the fixed-point iteration method. (k+ ¹ ) =RHS(V (k) The expression takes the form ), where RHS represents the function on the right side of the equation with respect to the instantaneous oil conduction rate, and the instantaneous oil conduction rate at the previous acquisition time is taken as the initial value V. (0) Repeatedly substitute and calculate until |V (k+ ¹ ) V (k) | / |V (k) |<10 -4For example, Newton's iteration method can be used to construct the residual function f(V) = V. RHS(V) is used to calculate the derivative of the residual function with respect to V, f'(V), according to the update formula V. (k+ ¹ ) =V (k) f(V (k) ) / f'(V (k) Iterate and update until the relative deviation is less than the preset deviation threshold.

[0058] For example, the processor can adaptively select the iteration method based on the current immersion length interval. In high-shear-rate sections with shorter immersion lengths, Newton's iteration method can be used to utilize its second-order convergence speed; in low-shear-rate sections with longer immersion lengths, fixed-point iteration method can be used to ensure iteration stability. For example, a maximum iteration count can also be set. When the number of iterations exceeds the maximum but the relative deviation is still not less than a preset deviation threshold, the current optimal estimate is output, and the solution at that moment is marked as approximately converged.

[0059] Finally, the capillary flow rate determination model couples the dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor in a product form, and implicitly correlates them with the instantaneous flow rate. This allows the model to simultaneously reflect multi-physics field effects such as pore size swelling, non-Newtonian shear thinning, temperature-dependent viscosity, thermally induced surface tension degradation, and tortuosity damping, avoiding the detection distortion caused by the single constant assumption in the classical Lucas-Washburn model. For example, the capillary flow rate determination model can be expressed as V = reff(L)·[γ(T)·cosθ·τ2(S) / (4·K·L·exp[B / (T)]] T∞)])]^(1 / n), where reff(L) is the dynamic effective aperture, γ(T) is the dynamic surface tension, τ2(S) is the tortuosity factor, K is the consistency coefficient, n is the flow index, and exp[B / (T)] [T∞)] represents the temperature dependence term in the composite dynamic viscosity, L is the current wetting length, and θ is the contact angle. For example, during the iterative solution process, the processor can sequentially calculate the dynamic effective aperture reff(L) = r0·[1] based on the current wetting length L. ηs·(L / Lmax)^m]、Local saturation S=L / Lmax、Tortuosity factor τ2(S)=τ2,0·[1 The parameters α·S^β], the local temperature field T(L,t), the dynamic surface tension γ(T) determined by the temperature field, and the composite dynamic viscosity ηdynamic are substituted into the implicit equation to complete a single iteration evaluation.

[0060] By determining the current wetting length corresponding to the current acquisition time from the seepage evolution data, the current wetting length is substituted into the capillary seepage oil conduction rate determination model. With the instantaneous oil conduction rate as the unknown quantity and the instantaneous oil conduction rate of the previous acquisition time as the initial value, fixed-point iteration or Newton iteration is performed for adaptive updating until the relative deviation value of two adjacent iterations is less than the preset deviation threshold. The instantaneous oil conduction rate at any acquisition time can be calculated by the processor without the need for analytical solution of implicit equations. This single-point implicit iteration scheme uses the determined instantaneous oil conduction rate of the previous acquisition time as the initial value, which can accelerate convergence and improve numerical stability. This makes the oil conduction rate determination method of liquid storage cotton adaptable to real flow conditions that are non-Newtonian, non-isothermal, porous, and multiphase.

[0061] As one implementation method, after adaptively updating the instantaneous oil conduction rate using the fixed-point iteration method or Newton's iteration method, the method further includes: The step size is recursively calculated based on the time interval of seepage evolution data. The model is determined according to the capillary seepage oil conduction rate to perform time-series recursion on the wetting length, and the preliminary wetting length corresponding to the next acquisition time is obtained. If the rate of change of the instantaneous oil guiding rate at the current acquisition time relative to the instantaneous oil guiding rate at the previous acquisition time exceeds the preset rate of change threshold, the baseline recursive step size is reduced to the target recursive step size, and the immersion length is re-time-recursively recursed with the target recursive step size to obtain the immersion length corresponding to the next acquisition time after correction. The corrected immersion length at the next sampling moment is used as the basis for determining the instantaneous oil conduction rate at the next sampling moment.

[0062] In this implementation, after adaptively updating the instantaneous oil conduction rate using the fixed-point iteration method or the Newton iteration method, the method may further include performing a time-series recursion on the immersion length to predict the immersion length at the next acquisition moment, and adaptively adjusting the recursion step size according to the rate of change of the instantaneous oil conduction rate.

[0063] Specifically, the baseline recursion step size is the time interval of the seepage evolution data, that is, the discrete time interval between the acquisition of the immersion length and the acquisition time by the high-speed camera or weighing sensor in the test fixture. For example, it can be set to 0.5s so that the time grid of the time-series recursion is aligned with the time grid of the hardware acquisition, providing a unified step size reference for the numerical integration of the capillary seepage conduction rate determination model. This baseline recursion step size can be directly read by the processor from the metadata of the seepage evolution data.

[0064] For example, the time-series recursion process is specifically a numerical calculation process in which the processor determines the differential equation described by the model based on the capillary seepage oil conduction rate, and calculates the wetting length at the next acquisition time from the wetting length at the current acquisition time and the instantaneous oil conduction rate. This process can be implemented by the processor executing the fourth-order Runge-Kutta method or other numerical integration algorithms, and feeding back the instantaneous oil conduction rate obtained by the single-point implicit iterative solution to the spatiotemporal evolution prediction of the wetting length, thereby forming a continuous solution link between acquisition times. Together with the aforementioned steps of determining the current wetting length from seepage evolution data and implicitly iteratively solving the instantaneous oil conduction rate, it forms a closed loop. Thus, the preliminary wetting length corresponding to the next acquisition time is the estimated value of the wetting length at the next time obtained by performing a time-series recursion with the benchmark recursion step size.

[0065] It should be understood that the rate of change of the instantaneous oil conduction rate at the current sampling moment relative to the instantaneous oil conduction rate at the previous sampling moment is the ratio of the difference between the instantaneous oil conduction rates at two adjacent moments to the instantaneous oil conduction rate at the previous moment. This rate of change is used to measure the degree of nonlinearity in the oil conduction process. The preset rate of change threshold is the critical value for determining whether to reduce the recursive step size. This threshold can be calibrated based on the seepage characteristics of the liquid storage cotton. The target recursive step size is the actual recursive step size value used after reducing the baseline recursive step size. When the baseline recursive step size is 0.5s, the target recursive step size can be reduced to 0.25s, thereby improving the numerical integration accuracy in the nonlinearly severe section. The wetting length corresponding to the next sampling moment after correction is the wetting length value of the next moment obtained by re-time recursion with the target recursive step size.

[0066] Subsequently, the processor reads the sampling time interval of the seepage evolution data as the numerical integration step size, i.e., the baseline recursive step size. It calls the control equation dL / dt=F(t,L) of the capillary seepage oil conduction rate determination model to advance the current wetting length by one step, thereby obtaining the preliminary predicted value of the wetting length at the next acquisition time. The evolution calculation of the wetting length is continued using the solved instantaneous oil conduction rate. This method can not only respond to hardware acquisition data point by point, but also perform model prediction for the acquisition gap. This recursive process is executed by the processor and constitutes the timing prediction link in the technical solution, solving the technical problem of error amplification in the seepage rate drastic change zone of traditional fixed step size integration.

[0067] For example, the fourth-order Runge-Kutta method can be used to discretize the differential equation. The recursive formula of the fourth-order Runge-Kutta method can be written as L{i+1}=L_i+(Δt / 6)·(k1+2k2+2k3+k4), where k1=F(t_i,L_i), k2=F(t_i+Δt / 2,L_i+Δt·k1 / 2), k3=F(t_i+Δt / 2,L_i+Δt·k2 / 2), k4=F(t_i+Δt,L_i+Δt·k3), Δt is the baseline recursive step size, and F(t,L) can be written as reff(L)·[γ(T)·cosθ·τ2(S) / (4·K·L·exp[B / (T)](T)). T∞)])]^(1 / n). For example, an explicit Euler method can also be used to simplify the calculation, i.e., L{i+1}=L_i+Δt·F(t_i,L_i). For example, when the baseline recursion step size is consistent with the sampling interval of the seepage evolution data (e.g., both are 0.5s), the time-series recursion result can be directly written into the seepage evolution data as the infiltration length for the next acquisition time.

[0068] The processor monitors the relative change in instantaneous oil conduction rate between two adjacent time points. If the rate of change of the instantaneous oil conduction rate at the current acquisition time relative to the instantaneous oil conduction rate at the previous acquisition time exceeds a preset rate of change threshold (e.g., a calibration value within the range of 0.2 to 0.5), it is determined that the current section has strong seepage nonlinearity and the numerical error at the reference step size is too large. Therefore, the recursive step size is reduced to the target recursive step size, and the wetting length is re-integrated. In other words, the reference recursive step size is reduced to the target recursive step size, and the wetting length is re-time-sequentially recursed using the target recursive step size to obtain the corrected wetting length corresponding to the next acquisition time. This can adaptively improve the accuracy of numerical solutions and solve the technical problem of error amplification in traditional fixed-step integration in regions with drastic changes in seepage rate.

[0069] For example, the target recursive step size can be half of the baseline recursive step size; that is, if the baseline recursive step size is 0.5s, the target recursive step size is 0.25s. For example, the immersion length range can also be used as an auxiliary trigger condition; when the immersion length is less than one-third of the critical length for complete saturation and the rate of change exceeds a preset rate of change threshold, the step size can be reduced. For example, after recursively recursing with a reduced step size, if the rate of change still exceeds the preset rate of change threshold, the step size can be further recursively reduced until the rate of change meets the requirement or reaches the minimum step size limit.

[0070] Subsequently, the processor passes the next moment's immersion length, obtained after adaptive step size correction, to the next round of the capillary seepage oil conduction rate determination model to determine the instantaneous oil conduction rate. This serves as the input source for determining the current immersion length from the seepage evolution data in this step. The time-series recursive prediction results are seamlessly connected to the single-point implicit iterative solution, forming a progressive solution loop based on the acquisition time. This ensures that the entire method for determining the oil conduction rate of the liquid storage cotton can maintain continuous and high-precision software solution between hardware acquisition intervals. This transmission process is completed by the processor in the memory data stream.

[0071] For example, the corrected infiltration length corresponding to the next acquisition time can be directly stored in the seepage evolution data buffer at the position corresponding to the next acquisition time, for subsequent use by the fixed-point iteration method or Newton's iteration method. Alternatively, the corrected infiltration length and the next acquisition time identifier can be combined into a new key-value pair and appended to the seepage evolution data sequence. Furthermore, after the actual hardware acquisition data arrives at the next acquisition time, the measured infiltration length can be compared and verified with the corrected infiltration length. If the deviation is less than the allowable range, the measured value is updated; otherwise, the corrected value is used as a provisional input for continued calculation.

[0072] By using the time interval of seepage evolution data as a benchmark for recursive step size, the model determines the initial wetting length by time-series recursion based on the capillary seepage oil conduction rate. When the instantaneous oil conduction rate change exceeds a preset change rate threshold, the benchmark recursive step size is reduced to the target recursive step size and recursively obtained to obtain the corrected wetting length. The corrected wetting length is then used as the basis for determining the instantaneous oil conduction rate at the next acquisition moment. The processor can adaptively balance the efficiency and accuracy of numerical integration and automatically refine the step size in sections with severe seepage nonlinearity, thereby avoiding the prediction deviation of the wetting length caused by a fixed step size and ensuring the solution stability and accuracy of the capillary seepage oil conduction rate determination model under all working conditions.

[0073] As one implementation method, please refer to Figure 4 As shown, Figure 4 A schematic flowchart of a method for determining the oil conduction rate of a liquid storage cotton provided in this disclosure is shown. After determining the instantaneous oil conduction rate of the liquid storage cotton based on the capillary seepage oil conduction rate determination model and seepage evolution data, the method further includes: S401. Based on the instantaneous oil conduction rate, determine at least one of the following: cumulative liquid conduction volume of the liquid storage cotton, saturation evolution curve, and average oil conduction rate. S402. Based on at least one of the cumulative liquid conduction volume, saturation evolution curve and average oil conduction rate, compare it with the corresponding preset selection threshold, and output the selection evaluation result of the liquid storage cotton.

[0074] In this embodiment, the cumulative liquid conduction volume of the liquid storage cotton is the total amount of target liquid transported from the liquid storage cotton to the heating element within a specified time period during the seepage process or the operation of the heating component. This parameter can be obtained by integrating the instantaneous oil conduction rate over the sampling time, reflecting the total liquid supply capacity of the liquid storage cotton on a macroscopic scale. The saturation evolution curve is a function curve showing the change of local saturation inside the liquid storage cotton with the sampling time. Local saturation can be approximately characterized by the ratio of the wetting length to the critical length for complete saturation. The role of the saturation evolution curve is to reflect the filling progress and spatial distribution evolution of the target liquid in the porous channels of the liquid storage cotton. The average oil conduction rate is the average rate at which the liquid storage cotton transports the target liquid within a specified time period. This parameter can be obtained by dividing the cumulative liquid conduction volume by the duration of the specified time period, or by averaging the instantaneous oil conduction rate over the specified time period, providing a single comprehensive quantitative indicator of the liquid supply capacity of the liquid storage cotton.

[0075] The preset selection threshold is a benchmark value used to judge whether the oil conduction performance of the liquid storage cotton is qualified. It may include at least one of the following: cumulative liquid conduction volume threshold, saturation achievement time threshold, and average oil conduction rate threshold. The above thresholds can be calibrated according to the liquid supply requirements of the heating component to the liquid storage cotton. The selection evaluation result is a conclusive output characterizing whether the liquid storage cotton is suitable for a specific heating component. This result can be generated by the processor and used to guide the screening and matching of liquid storage cotton.

[0076] Subsequently, using the instantaneous oil conduction rate calculated by the capillary seepage oil conduction rate determination model as the data source, key quantitative indicators for evaluating the oil conduction performance of the liquid storage cotton are further derived and calculated. These are multi-dimensional evaluation indicators that can be used for selection decisions, allowing for objective quantitative comparison of the liquid storage cotton in laboratory testing or production line screening. This determination process relies on the processor's further calculation of the seepage evolution data and the instantaneous oil conduction rate. For example, the cumulative liquid conduction volume can be obtained by numerically integrating the instantaneous oil conduction rate over the collection time interval; the local saturation can be calculated based on the wetting length corresponding to each collection time, and the change of local saturation with the collection time can be plotted as a saturation evolution curve; the average oil conduction rate can be obtained by taking the arithmetic mean of the instantaneous oil conduction rate over a specified time period; and the cumulative liquid conduction volume, saturation evolution curve, and average oil conduction rate can be determined simultaneously and combined to form a profile of the oil conduction performance of the liquid storage cotton.

[0077] Based on at least one of the cumulative liquid conduction volume, saturation evolution curve, and average oil conduction rate, the selection evaluation result of the liquid storage cotton is output by comparing it with the corresponding preset selection threshold. In other words, the aforementioned quantitative indicators are compared with the preset selection threshold in terms of magnitude or trend, and the processor generates a selection evaluation conclusion based on the comparison result. This transforms the quantitative data of the oil conduction performance of the liquid storage cotton into a judgment result that can be used for production decision-making, solving the technical problem that traditional liquid storage cotton selection relies on manual experience and lacks a unified quantitative standard.

[0078] For example, the cumulative liquid conduction volume can be compared with a cumulative liquid conduction volume threshold. When the cumulative liquid conduction volume is greater than or equal to the cumulative liquid conduction volume threshold, a first-type selection evaluation result indicating that the liquid storage cotton is qualified is output. For example, the average oil conduction rate can be compared with an average oil conduction rate threshold. When the average oil conduction rate falls within the average oil conduction rate threshold range, a second-type selection evaluation result indicating that the liquid storage cotton is suitable for a specific heating component is output. For example, the time required to reach a preset saturation level can be extracted from the saturation evolution curve, and this time can be compared with a saturation attainment time threshold. When the time is less than or equal to the saturation attainment time threshold, a third-type selection evaluation result indicating that the liquid storage cotton has a fast liquid supply response is output. For example, a comprehensive weighted score can be applied to the cumulative liquid conduction volume, average oil conduction rate, and saturation evolution curve. When the comprehensive score is greater than or equal to a preset score threshold, a fourth-type selection evaluation result indicating that the liquid storage cotton is preferred is output.

[0079] By quantifying and comparing the instantaneous oil conduction rate with the threshold, the processor can automatically output the selection evaluation results of the liquid storage cotton. This allows for an objective, quantifiable, and reproducible assessment of the oil conduction performance of the liquid storage cotton without the need for human experience-based judgment, providing a reliable technical basis for the selection and matching of liquid storage cotton for electronic heaters.

[0080] As one implementation method, after determining the instantaneous oil conduction rate of the storage cotton based on the capillary seepage oil conduction rate determination model and seepage evolution data, the method further includes: The oil conduction status of the heating component is monitored based on the instantaneous oil conduction rate. When the instantaneous oil conduction rate is lower than the preset rate threshold, the oil conduction status is a dry-burn risk state. In response to the oil guiding state being a dry-burning risk state, a control command is output. This control command is used to control the heating component to reduce the heating power to prevent the heating component from dry-burning.

[0081] This implementation method allows the instantaneous oil transfer rate to be applied to the operation control of the heating component. The heating component is an electronic heater containing a heating element and a liquid storage cotton. When the heating element is energized, it generates heat that heats the target liquid that is directed to the heating element. Under the action of capillary driving force, the liquid storage cotton transports the target liquid from the distal end of the liquid storage cotton to the end where the heating element is located.

[0082] In this embodiment, the oil guiding state refers to the real-time status of the liquid supply from the storage cotton to the heating element during the operation of the heating component. This state can be quantitatively characterized by the instantaneous oil guiding rate calculated by the aforementioned capillary seepage oil guiding rate determination model. Specifically, the dry-burning risk state is a situation in the oil guiding state where insufficient replenishment of the target liquid to the surface of the heating element due to an excessively low instantaneous oil guiding rate poses a risk of overheating and scorching of the heating element. The preset rate threshold is a critical value of the instantaneous oil guiding rate used to determine the dry-burning risk state. This threshold can be calibrated based on the rated liquid supply capacity of the heating component. For example, it can be set as a calibration value within the range of 20% to 50% of the average instantaneous oil guiding rate of the storage cotton under rated liquid supply conditions.

[0083] In other words, when the liquid storage cotton is assembled into the heating component and is in actual suction mode, the instantaneous oil conduction rate output by the capillary flow oil conduction rate determination model is continuously acquired, and the instantaneous oil conduction rate is compared with the preset rate threshold to determine the current oil conduction state category of the heating component. In this way, the technical parameters calculated by the aforementioned software algorithm can be transformed into a real-time diagnostic basis for the operational risks of physical equipment, so that the oil conduction rate data, which was originally only used for laboratory evaluation, can directly serve the safety control of the electronic heater.

[0084] For example, the instantaneous oil conduction rate can be read at fixed time intervals during each suction process of the heating component. If the instantaneous oil conduction rate is lower than a preset rate threshold for multiple consecutive reading cycles, the oil conduction state is determined to be at risk of dry burning. Alternatively, the instantaneous oil conduction rate can be processed by a moving average filter to obtain a smoothed oil conduction rate, and then compared with a preset rate threshold. When the smoothed oil conduction rate falls below the preset rate threshold, the oil conduction state is determined to be at risk of dry burning. Another example is the combined determination using the rate of change of the instantaneous oil conduction rate. When the instantaneous oil conduction rate is lower than a preset rate threshold and the rate of change is negative, the oil conduction state is determined to be at risk of dry burning.

[0085] When the monitoring module determines that the heating component is at risk of dry burning, it sends a control command to the power management unit of the heating component to reduce the heating power of the heating element. For example, the heating component can be controlled to linearly reduce the current heating power to a target power within the range of 30% to 70% of the current heating power. Alternatively, a stepped power reduction strategy can be adopted, where the heating power is reduced to 80% of the current value when the dry burning risk is first determined, and further reduced to 60% of the current value if the dry burning risk persists in subsequent cycles. For example, while reducing the heating power, the power supply to the heating element can be paused and a liquid replenishment timer can be started. The heating power is restored after the timer expires. Furthermore, by adjusting the electrical power parameters in the physical world, the temperature of the heating element is suppressed from continuing to rise, thus solving the technical problem of traditional heaters causing dry burning and scorching due to the inability to sense the liquid supply status. This control process relies on the processor of the liquid replenishment cotton's oil conduction rate determination device, directly controlling the hardware actuators of the heating component, constituting a technical means rather than an abstract judgment.

[0086] By monitoring the oil conduction status of the heating component in real time and controlling the heating power in a closed loop, the instantaneous oil conduction rate calculated by the capillary flow oil conduction rate determination model can be used to directly trigger the anti-dry-burn protection, thereby effectively avoiding the dry-burn damage of the heating component due to insufficient liquid supply and improving the safety and lifespan of the electronic heater.

[0087] As one implementation method, the capillary flow rate determination model is constructed as follows: An ideal straight pipe reference model is established, which simplifies the porous channels of the liquid storage cotton into rigid, constant-diameter, straight capillary tubes, and adopts the straight path assumption that the fluid properties and pipe characteristics are constant in the classical capillary flow theory. The dynamic correction factors are determined, including: dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor. The composite dynamic viscosity is obtained by coupling non-Newtonian power-law viscosity with temperature viscosity dependence. Non-Newtonian power-law viscosity is used to characterize the rheological properties of the target liquid under high shear rate, and temperature viscosity dependence is used to characterize the thermal thinning properties of the target liquid whose viscosity decreases exponentially with increasing temperature. The dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor are replaced by the constant pore size, constant viscosity, constant surface tension, and straight path assumptions in the ideal straight pipe reference model, respectively. They are then substituted into the ideal straight pipe reference model in a product coupling manner to obtain the capillary seepage oil conduction rate determination model.

[0088] In this implementation, the processor in the oil conduction rate determination device for the liquid storage cotton can call computer-executable instructions stored in memory. Using the rigid straight-tube equation framework provided by the classical Lucas-Washburn capillary flow theory as the theoretical benchmark, it loads the dynamic variables of multiple physics fields that are not directly observable during the actual liquid storage cotton flow process into the theoretical framework through a product coupling of four dynamic correction factors: dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor. This generates a corrected capillary flow oil conduction rate determination model that reflects the real flow state of non-Newtonian, non-isothermal, porous, and multiphase flows. Furthermore, the constant parameter assumptions in the traditional ideal straight-tube model can be replaced with dynamic variables consistent with actual working conditions. This allows the generated capillary flow oil conduction rate determination model to adapt to the actual working scenario of the electronic heater, solving the problem of severe measurement distortion in the classical Lucas-Washburn model under porous media, non-isothermal, and non-Newtonian flow conditions. This differs from the traditional approach that requires the addition of temperature sensors or pore probes to capture dynamic variables.

[0089] Specifically, the ideal straight pipe reference model simplifies the porous channels of the liquid storage cotton into rigid, constant-diameter, straight capillaries, and adopts the reference model of the straight path assumption (i.e., the simplification assumption) in classical capillary flow theory, which assumes that fluid properties and pipe characteristics are constant. The role of the ideal straight pipe reference model is to provide the basic physical framework for the relationship between capillary pressure difference drive, viscous resistance, and the evolution of wetting length, so that subsequent dynamic correction factors can be replaced and coupled at the corresponding positions in the theoretical framework of the ideal straight pipe reference model, without having to derive the governing equations from scratch.

[0090] Specifically, the ideal straight pipe benchmark model adopts the initial setting of the classic Lucas-Washburn model, equating the tortuous porous medium to a single capillary with a constant cross-section and straight direction, while assuming that the viscosity, surface tension, and channel geometry of the target liquid remain constant throughout the seepage process. This setting serves to establish the simplest analytical benchmark, providing a reference point for subsequently identifying the four dimensions that need to be dynamically adjusted: the effective pore size of the channel with swelling and contraction, viscosity with shear rate and temperature, surface tension with temperature degradation, and path tortuosity with saturation.

[0091] Next, the dynamic correction factors can be determined. These are a set of parameters used to dynamically replace the constant parameters in the ideal straight pipe reference model. Specifically, the dynamic correction factors include the dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor.

[0092] The dynamic correction factor serves to correct the constant assumptions of the ideal straight pipe benchmark model in four dimensions: geometric channel, fluid viscosity, interfacial tension, and path tortuosity, thereby transitioning the model from an ideal rigid straight pipe to a real swollen porous medium. The composite dynamic viscosity is obtained by coupling non-Newtonian power-law viscosity with temperature-dependent viscosity. That is, the composite dynamic viscosity is not a single constant, but an apparent viscosity expression composed of a shear rate-dependent power-law term and a temperature-dependent exponential term, thus simultaneously incorporating the viscosity decay of the heated liquid under high shear gradients within the capillary channel and the viscosity-thermal thinning caused by heat transfer from the heating element. The composite structure ensures that the viscosity-side correction covers the time-varying effects of both shear rate and temperature, corresponding to the limitation in the independent claim that the composite dynamic viscosity is used to characterize the apparent viscosity of the target liquid as a function of shear rate and temperature.

[0093] Non-Newtonian power-law viscosity can be expressed as the product of the consistency coefficient and the flow index minus one power of the shear rate. The flow index can be calibrated in the range of 0.6 to 0.95, and a flow index less than one indicates shear thinning. Temperature-dependent viscosity can be expressed as an exponential form with the divergence temperature as the denominator, the difference between the local temperature and the divergence temperature as the denominator, and the viscosity-temperature dependence parameter as the numerator. The viscosity-temperature dependence parameter is greater than zero, and the divergence temperature is less than the local temperature. Composite dynamic viscosity can be fully expressed as the consistency coefficient multiplied by the flow index minus one power of the shear rate, then multiplied by the viscosity-temperature dependence parameter (e) by the natural constant e, divided by the difference between the local temperature and the divergence temperature raised to the power of e. For example, the formula for calculating composite dynamic viscosity can be proven as: η_dynamic = K·γ^(n-1)·e^[B / (TT∞)], where η_dynamic is the composite dynamic viscosity, K is the consistency coefficient, γ is the shear rate, n is the flow index, B is the viscosity-temperature dependence parameter, T is the local temperature, and T∞ is the divergence temperature.

[0094] The dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor are replaced by the constant pore size, constant viscosity, constant surface tension, and straight path assumptions in the ideal straight pipe reference model, respectively, using a product coupling method. This yields a capillary flow rate determination model. In other words, the processor can map and replace the original constant pore size, constant viscosity, constant surface tension, and straight path assumptions in the ideal straight pipe reference model using the product coupling mathematical form, thereby generating a new capillary flow rate determination model within the theoretical framework of the ideal straight pipe reference model. That is, the dispersed dynamic factors are uniformly assembled into the reference equation skeleton, allowing the capillary flow rate determination model to simultaneously accommodate geometric swelling correction, two-dimensional viscosity correction, interfacial thermal degradation correction, and path tortuosity damping correction, forming a fully differential physical reconstruction.

[0095] For example, establishing an ideal straight pipe reference model can be achieved by the processor calling the initial form of the classic Lucas-Washburn model, setting the porous channels of the liquid storage cotton as straight cylindrical capillary tubes with a constant radius, and setting the target liquid as a Newtonian fluid whose viscosity and surface tension do not change with position and time, thus obtaining a reference governing equation that only includes the assumptions of constant pore size, constant viscosity, constant surface tension, and a straight path. Alternatively, establishing an ideal straight pipe reference model can also be achieved by directly writing the initial constants of the reference model using the dry geometric parameters of the same batch of liquid storage cotton and the physical properties of the target liquid at room temperature during the calibration phase, for use when replacing dynamic correction factors later.

[0096] For example, the dynamic effective aperture can be determined as follows: the processor reads the initial effective radius, swelling and shrinkage coefficient, and critical length for full saturation from the calibration parameter library, and substitutes them into the expression for dynamic effective aperture. The dynamic effective aperture can be expressed as the initial effective radius multiplied by (a) minus the swelling and shrinkage coefficient multiplied by the ratio of the wetting length to the critical length for full saturation, raised to a nonlinear power. The swelling and shrinkage coefficient can be a calibration value in the range of 0.05 to 0.3. As an example and not a limitation, the formula for dynamic effective aperture can be proven to be: r_eff(L)=r0·[1] η_s·(L / L_max)^m], where r_eff(L) is the dynamic effective pore size, r0 is the initial effective radius, η_s is the swelling and shrinkage coefficient, L is the wetting length, L_max is the critical length for complete saturation, and m is the swelling nonlinear exponent.

[0097] For example, the tortuosity factor can be determined as follows: the processor reads the initial tortuosity damping factor, saturation sensitivity coefficient, and nonlinearity exponent from the calibration parameter library, substitutes them into the tortuosity factor expression, and the tortuosity factor can be expressed as the initial tortuosity damping factor multiplied by (a) minus the saturation sensitivity coefficient multiplied by the local saturation nonlinearity exponent raised to the power of (a). The initial tortuosity damping factor can take typical values ​​in the range of 0.62 to 0.85, the saturation sensitivity coefficient can take typical values ​​in the range of 0.15 to 0.35, and the nonlinearity exponent can take typical values ​​in the range of 1.5 to 2.5. The formula for calculating the tortuosity factor can be proven as: τ2(S)=τ2,0·[1] α·S^β], where τ2(S) is the tortuosity factor, τ2,0 is the initial tortuosity damping factor, α is the saturation sensitivity coefficient, S is the local saturation, and β is the nonlinear exponent.

[0098] For example, the method for determining the composite dynamic viscosity and dynamic surface tension can also be as follows: the processor estimates the local temperature field based on the heating time of the heating element. The local temperature field can be expressed as the ambient temperature baseline at the far end of the liquid storage cotton plus the temperature rise amplitude multiplied by the negative temperature decay characteristic coefficient of the natural constant e multiplied by the wetting length power. The temperature rise amplitude can be expressed as the maximum temperature rise amplitude at the near end of the heating element multiplied by the negative heating time minus the natural constant e divided by the heating time constant power. Then, the local temperature field is written back to the composite dynamic viscosity and dynamic surface tension respectively.

[0099] As an example, not a limitation, dynamic surface tension can be expressed as the reference surface tension multiplied by (a) minus the surface tension temperature coefficient multiplied by the temperature rise amplitude multiplied by the negative temperature decay characteristic coefficient of the natural constant e multiplied by the wetting length raised to the power of the reference surface tension. The reference surface tension and the surface tension temperature coefficient are basic physical property parameters calibrated once through standard interface testing. The formula for calculating dynamic surface tension can be corroborated as: γ(T) = γ0·[1] δ·ΔT(t)·e^( λL)], where γ(T) is the dynamic surface tension, γ0 is the reference surface tension, δ is the surface tension temperature coefficient, ΔT(t) is the temperature rise amplitude, λ is the temperature decay characteristic coefficient, and L is the wetting length; the temperature field is expressed as T(L,t)=T0+ΔT(t)·e^( λL), where T(L,t) is the local temperature field, T0 is the far-end ambient temperature baseline, ΔT(t)=ΔT_max·[1 e^( t / τ_h)], ΔT_max is the maximum temperature rise near the heating element, t is the heating time, and τ_h is the heating time constant.

[0100] For example, the specific actions of substituting into the ideal straight pipe reference model in a product coupling manner can be as follows: the processor places the dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor at the pore size position, viscosity position, surface tension position, and path damping position of the reference equation skeleton, respectively, and couples them in a product form to obtain the capillary flow oil conduction rate determination model. For example, the governing equation of this capillary flow oil conduction rate determination model can be expressed as the instantaneous oil conduction rate V, which is equal to the dynamic effective pore size r_eff(L) multiplied by the dynamic surface tension multiplied by the contact angle cosine multiplied by the tortuosity factor within square brackets, divided by the viscosity-temperature dependent parameter multiplied by the consistency coefficient multiplied by the wetting length multiplied by the natural constant e, divided by the difference between the local temperature and the divergence temperature, and the whole outside the square brackets is taken as a power of the flow index. That is, the governing equation is: V=r_eff(L)·[γ(T)·cosθ·τ2(S) / (4·K·L·e^[B / (TT∞)])]^(1 / n), where V is the instantaneous oil conduction rate and θ is the contact angle.

[0101] For example, the substitution method using product coupling can also be: the processor updates the values ​​of the four dynamic correction factors in real time during each solution iteration, and resubstitutes the updated factors into the control equation to achieve adaptive correction during the immersion length advancement process.

[0102] By using an ideal straight pipe reference model as the theoretical framework, and substituting the dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor in a product coupling manner to replace the assumptions of constant pore size, constant viscosity, constant surface tension, and straight path respectively, a capillary flow oil conduction rate determination model adapted to real working conditions can be obtained. This reduces the errors of the classic Lucas-Washburn model (19.7% under normal temperature conditions, 37.2% under high temperature conditions, and 15.6% under high saturation conditions) to 3.1% to 5.8% in this real-time example, improving the accuracy and engineering applicability of determining the oil conduction rate of the liquid storage cotton in the electronic heater.

[0103] As one implementation method, this method also includes: The heating time of the heating element in the heating assembly is obtained, and the end where the heating element is located is taken as the near end of the heat source. The temperature rise near the heat source is determined based on the heating time, wherein the temperature rise changes dynamically with the heating time. Using the temperature rise amplitude as the temperature boundary near the heat source, the local temperature field is estimated along the wetting length of the liquid storage cotton in an exponential decay manner. The local temperature field decays exponentially with the increase of the wetting length. Based on the local temperature field, the capillary flow rate is determined to determine the composite dynamic viscosity and dynamic surface tension in the model. The composite dynamic viscosity changes with the temperature in the local temperature field, and the dynamic surface tension degrades thermally with the temperature in the local temperature field.

[0104] In this implementation, the heating component is an electronic heater containing a heating element and a liquid reservoir. The heating element generates heat upon energization and conducts it through the liquid reservoir. The heating time is the duration from the moment the heating element is energized to the current moment, which can be obtained by the controller of the heating component. The heat source proximal end is the end of the liquid reservoir closest to the heating element, which directly receives the heat transferred from the heating element and corresponds to the zero point in the liquid reservoir's immersion length coordinate system. The temperature rise amplitude is the increase in temperature of the heat source proximal end relative to the ambient baseline during the current heating time. This value dynamically changes with the heating time, characterizing the dynamic process of heat transfer from the heating element to the liquid reservoir.

[0105] The local temperature field is a function of the temperature distribution at various locations along the wetting length within the liquid storage cotton. This distribution decays exponentially along the wetting length under the temperature boundary conditions near the heat source. This transforms the internal temperature distribution of the liquid storage cotton, which cannot be directly measured, into a continuous field that can be estimated by software algorithms. This provides temperature input for determining the composite dynamic viscosity and dynamic surface tension, and this estimation process does not rely on temperature sensors. The composite dynamic viscosity is a parameter characterizing the apparent viscosity of the target liquid as a function of shear rate and temperature; its value decreases as the temperature in the local temperature field increases. The dynamic surface tension is a parameter characterizing the thermally induced degradation characteristics of the target liquid; its value decreases as the temperature in the local temperature field increases, and this characteristic also affects the magnitude of the capillary driving force.

[0106] In scenarios where liquid-storing cotton is used in electronic heaters, the heating time of the heating element in the heating assembly is first obtained, and the end where the heating element is located is taken as the near end of the heat source. That is, when the liquid-storing cotton is assembled into the heating assembly and is in actual suction mode, the cumulative energization time of the heating element is obtained by the controller of the electronic heater, and the origin of the geometric coordinate system of the liquid-storing cotton is anchored to the end face where the heating element is located. This allows the establishment of time input and spatial coordinate origin for the estimation of the local temperature field, so that the temperature field model can be aligned with the actual physical structure.

[0107] For example, a timer can be started at the beginning of each power-on cycle of the heating element, and the timer value can be read at fixed time intervals as the heating time. For example, the controller can also integrate the power-on period according to the power curve of the heating element to obtain the equivalent heating time. For example, the heating time in the current suction cycle can be added to the heating time in the previous suction cycle to obtain the cumulative heating time experienced by the liquid storage cotton.

[0108] The temperature rise amplitude near the heat source is determined based on the heating time, where the temperature rise amplitude dynamically changes with the heating time. That is, using the heating time as an input variable, the temperature increase value of the heat source near the current moment relative to the ambient baseline is calculated through the temperature rise model, and the timing data of the controller is converted into temperature boundary values ​​to reflect the dynamic response process of heat transfer of the heating element. This determination process is executed by the processor. For example, the temperature rise amplitude ΔT(t) can be expressed as ΔT(t) = ΔTmax·[1] exp( [t / τh], where ΔTmax is the maximum temperature rise near the heat source, τh is the heating time constant, and t is the heating time. ΔTmax and τh can be obtained by calibrating the temperature response curves of heating components in the same batch under known power. For example, the temperature rise can also be established as a piecewise function of the heating time, using a rapid rise model in the initial heating stage and a saturation approaching stage model in the middle heating stage. For example, the temperature rise can also be corrected by combining the real-time power of the heating element, adjusting the value of ΔTmax accordingly when the real-time power changes.

[0109] Using the temperature rise amplitude as the temperature boundary near the heat source, the local temperature field is estimated along the wetting length of the liquid storage cotton according to the exponential decay method. That is, the aforementioned determined temperature rise amplitude is regarded as the temperature boundary condition of the near end of the heat source of the liquid storage cotton. Based on the law of exponential decay of temperature along the heat transfer direction in the heat conduction theory, the temperature value at any wetting length position inside the liquid storage cotton is calculated, thereby obtaining a continuous local temperature field distribution. Temperature field information can be obtained without placing temperature sensors inside the liquid storage cotton, solving the technical problem that traditional solutions cannot obtain temperature distribution under non-isothermal conditions. This estimation process is solved in real time by the processor according to the heating time.

[0110] For example, the local temperature field can be expressed as T(L,t) = T0 + ΔT(t)·exp( λL), where T0 is the baseline ambient temperature at the far end of the liquid storage cotton, ΔT(t) is the temperature rise amplitude at the near end of the heat source, λ is the temperature decay characteristic coefficient, and L is the wetting length. The temperature decay characteristic coefficient λ can be obtained through experimental calibration of the temperature distribution of the same batch of liquid storage cotton. For example, a correction term considering axial heat conduction of the liquid storage cotton can be superimposed on the exponential decay to improve the accuracy of temperature field estimation for long-length liquid storage cotton. For example, the local temperature field can also be represented as a discrete temperature lookup table, with the temperature values ​​of each grid point pre-calculated along the wetting length direction at a fixed step size for real-time table lookup.

[0111] Based on the local temperature field, the composite dynamic viscosity and dynamic surface tension in the capillary flow rate determination model are determined. Specifically, the composite dynamic viscosity changes with the temperature in the local temperature field, and the dynamic surface tension thermally degrades with the temperature in the local temperature field. That is, using the temperature values ​​at each location in the previously estimated local temperature field, the composite dynamic viscosity and dynamic surface tension at that location are calculated respectively, and the calculation results are written back into the capillary flow rate determination model. This allows the capillary flow rate determination model to reflect the influence of the temperature field on fluid properties, correcting the deficiency of the classical model in not being able to incorporate time-varying temperature fields. As a result, the error of the classical Lucas-Washburn model under high-temperature conditions is reduced from 37.2% to 5.3% in this real-time example.

[0112] As an example, and not a limitation, the composite dynamic viscosity can be determined as follows: ηdynamic=K·γ^(n 1)·exp[B / (T(L,t) T∞)], where T(L,t) is the local temperature field, K is the consistency coefficient, γ is the shear rate, n is the flow index, B is the viscosity-temperature dependent parameter, and T∞ is the divergence temperature. For example, the dynamic surface tension can be determined by realizing the formula γ(T)=γ0·[1 δ·ΔT(t)·exp( λL), where γ0 is the reference surface tension and δ is the surface tension temperature coefficient. For example, the local temperature field can be discretized into multiple grid points along the wetting length, and the composite dynamic viscosity and dynamic surface tension corresponding to each grid point can be calculated point by point. These are then organized into a parameter distribution array corresponding to the wetting length for use by the capillary flow oil conduction rate determination model. For example, the local temperature field can also be processed by a time-dimensional moving average to obtain a smoothed temperature field, and the smoothed composite dynamic viscosity and dynamic surface tension can be determined accordingly to reduce the disturbance of instantaneous temperature fluctuations to the model solution.

[0113] By obtaining the heating time of the heating element when the liquid storage cotton is applied to the heating component, and estimating the local temperature field using the temperature rise amplitude as the temperature boundary near the heat source in an exponential decay manner, the capillary flow oil conduction rate is determined based on the local temperature field to determine the composite dynamic viscosity and dynamic surface tension in the model. This allows for the software reconstruction of the non-isothermal temperature field inside the liquid storage cotton without adding a temperature sensor, and the temperature field information is fed back to the oil conduction rate determination model in real time. This enables the instantaneous oil conduction rate calculation result to reflect the temperature gradient effect caused by the dynamic heat transfer of the heating element during the actual suction process, effectively correcting the serious distortion of the classic Lucas-Washburn model under high-temperature conditions and improving the accuracy of the oil conduction rate determination of the heating component.

[0114] This disclosure provides a parameter calibration method for determining the oil conduction rate of a liquid storage cotton. The physical property parameters involved in this model can be divided into two categories: the first category consists of basic physical property parameters that can be directly obtained from material data or standard tests. These parameters are calibrated through one-time standard rheological and interface tests, and do not need to be measured repeatedly in each subsequent test; the second category consists of liquid storage cotton medium parameters and temperature field parameters obtained by this model from existing seepage evolution data. The calibration process includes: an initial stage approximating Newtonian isothermal seepage using linear regression of the square of the wetting length against time to give an initial value of the initial effective radius; nonlinear least square fitting of the complete wetting length curve to optimize the swelling and tortuosity parameters; and fitting and establishing empirical temperature field models under normal temperature and high temperature conditions respectively.

[0115] Once calibration is complete, subsequent production only requires existing hardware to collect seepage evolution data, and the software can automatically calculate all quantitative indicators such as instantaneous oil conduction rate, saturation, and temperature field. This method also includes calibrating the basic physical properties of the target liquid in one step through standard rheological and interface tests, and inverting the parameters of the storage cotton medium and temperature field parameters from the seepage evolution data. This calibration process relies on the collaboration of standard experimental equipment and processor software algorithms, constituting a prerequisite for making the model engineering-feasible in the technical solution. It solves the technical problem that traditional model parameter acquisition relies on repeated experiments and cannot accommodate multi-physics coupling.

[0116] The fundamental physical properties of the target liquid were calibrated in one step using standard rheological and interfacial tests. Standard rheological tests are experimental methods that measure the viscosity response curves of the target liquid at different shear rates and temperatures using a rotational rheometer or capillary rheometer. Interfacial tests are experimental methods that measure the surface tension of the target liquid and its temperature dependence using the pendant drop method or Williams plate method. Fundamental physical properties are a set of constants describing the rheological and interfacial characteristics of the target liquid; these parameters are classified as the first type of parameters that can be obtained in one step through standard tests and do not require subsequent repeated measurements.

[0117] Offline measurements of the target liquid are performed using standardized experimental equipment to extract and store the required fluid constitutive and interface parameters for the model. This calibration method isolates the inherent properties of the fluid from online seepage testing, allowing subsequent inversion calibration based on seepage evolution data to focus solely on the reservoir medium and temperature field parameters, thus reducing the dimensionality and computational burden of online calibration. This testing process relies on data interaction between the physical experimental equipment and the processor. For example, after determining the production batch of the target liquid, a rotational rheometer can be used to scan the shear rate within a temperature range of 25°C to 80°C to obtain the consistency coefficient and flow index. For example, a pendant drop interfacial tensiometer can be used to measure the surface tension of the target liquid at a reference temperature and the rate of decrease with increasing temperature. For example, the test results of standard rheological and interface tests can also be written into a processor-accessible physical property parameter library for use by the capillary seepage rate determination model during solution processing.

[0118] The power-law parameter and temperature-viscosity parameter of the target liquid are used to jointly determine the composite dynamic viscosity. The power-law parameter of the target liquid is a constitutive parameter describing the non-Newtonian shear-thinning characteristics of the target liquid, and may include the consistency coefficient K and the flow index n, where the flow index n is less than 1 to characterize shear-thinning, and the flow index n can take a calibration value in the range of 0.6 to 0.95. The temperature-viscosity parameter describes the characteristic that the viscosity of the target liquid decreases exponentially with temperature, and may include the viscosity-temperature dependence parameter B and the divergence temperature T∞, where the viscosity-temperature dependence parameter B is greater than 0, and the divergence temperature T∞ is less than the local temperature. The definition and function of the composite dynamic viscosity are as described in the previous method embodiments. It is a parameter characterizing the apparent viscosity of the target liquid as a function of shear rate and temperature, and the composite dynamic viscosity is expressed as ηdynamic = K·γ^(n 1)·exp[B / (T [T∞)], where γ is the shear rate and T is the local temperature. This means that the consistency coefficient K and flow index n obtained from standard rheological tests, along with the temperature viscosity parameter B and divergence temperature T∞, are used as input constants for the composite dynamic viscosity formula. This allows the composite dynamic viscosity to simultaneously reflect both shear rate dependence and temperature dependence. The aforementioned target liquid power-law parameters and temperature viscosity parameters serve to provide dynamic corrections on the fluid viscosity side for the capillary flow oil conduction rate determination model, correcting the classical model's assumption of constant viscosity.

[0119] For example, the power-law parameters and temperature-viscosity parameters of the target liquid can be substituted into the composite dynamic viscosity formula, and the composite dynamic viscosity at the current location can be calculated by combining the real-time shear rate and local temperature. Alternatively, a mapping table can be established between the power-law parameters and temperature-viscosity parameters of the target liquid and different heated liquid formulations, allowing the corresponding parameters to be directly called when the target liquid is changed.

[0120] The reference surface tension and the surface tension temperature coefficient are used to jointly determine the dynamic surface tension. The reference surface tension is the surface tension value of the target liquid under the reference temperature condition, denoted as γ0. The surface tension temperature coefficient is a parameter characterizing the slope of the surface tension as the temperature increases, denoted as δ. The definition and function of dynamic surface tension are as described in the previous method embodiments. It is a parameter characterizing the thermal degradation characteristics of the target liquid, and the dynamic surface tension is expressed as γ(T) = γ0·[1] δ·ΔT(t)·exp( λL)], where ΔT(t) is the temperature rise amplitude near the heating element, λ is the temperature decay characteristic coefficient, and L is the immersion length.

[0121] That is, the reference surface tension γ0 and surface tension temperature coefficient δ obtained from standard interface testing are used as constants in the dynamic surface tension formula, enabling the dynamic surface tension to reflect thermal degradation caused by the temperature field. The aforementioned reference surface tension and surface tension temperature coefficient provide a dynamic correction for the surface tension side of the capillary flow rate determination model, and this dynamic surface tension also constitutes one of the sources of capillary driving force. For example, the reference surface tension and surface tension temperature coefficient can be substituted into the dynamic surface tension formula, and the dynamic surface tension at the current location can be calculated by combining it with the local temperature field. For example, interface testing can also be repeated at different reference temperatures, and the surface tension temperature coefficient δ can be extracted through linear or nonlinear regression.

[0122] The contact angle characterizes the wetting properties between the target liquid and the storage cotton, and participates in the calculation of capillary driving force in the capillary flow rate determination model. The contact angle is the liquid-solid-gas three-phase contact angle formed between the target liquid and the solid surface of the storage cotton. This angle reflects the spreading ability of the target liquid on the porous channel surface of the storage cotton. The definition and function of the capillary driving force, as described in the previous method embodiments, is the pressure difference generated by surface tension in the porous channel of the storage cotton. Physically, this can be expressed as the capillary pressure difference equal to the product of four times the dynamic surface tension and the cosine of the contact angle divided by the effective pore size of the porous channel, i.e., ΔP_cap = 4γ(T)cosθ / d.

[0123] In other words, the contact angle of the target liquid on the surface of the reservoir cotton fibers is measured using a standard interface test. This contact angle is then substituted into the capillary driving force formula and the core governing equation of the capillary flow oil conduction rate determination model. The contact angle quantifies the contribution of the wettability of the target liquid to the reservoir cotton on the driving pressure difference, enabling the model to accurately reflect the differences in capillary driving between different combinations of reservoir cotton and target liquid. For example, a contact angle meter can be used to perform a seated drop test on reservoir cotton slices to obtain the contact angle between the target liquid and the reservoir cotton. Alternatively, the contact angle can be estimated using Young's equation based on the surface energy of the reservoir cotton fibers and the surface tension of the target liquid. Furthermore, a mapping can be established between the contact angle and different reservoir cotton types, allowing the corresponding contact angle value to be directly used when replacing the reservoir cotton.

[0124] Using the seepage evolution data acquired by the data acquisition component, the processor executes nonlinear fitting or piecewise regression algorithms to extract the geometric evolution parameters and empirical temperature field parameters of the liquid storage cotton that cannot be directly obtained from standard rheological and interface tests. The purpose of this inversion method is to establish the calibration process entirely on the existing hardware-acquired wetting length and acquisition time data, without the need for additional pore probes or temperature sensors, achieving model parameter acquisition without additional hardware dependence. This inversion process is completed by the processor executing software algorithms, constituting a key link in the technical solution to adapt the model to the specific liquid storage cotton. For example, the initial seepage stage can be approximated as Newtonian isothermal seepage, and linear regression can be performed on the square of the wetting length against the acquisition time. The slope provides an initial value for the ratio of the initial effective radius to the reference viscosity, which, combined with the calibrated reference viscosity, yields the initial effective radius. For example, nonlinear least-squares fitting can be performed on the complete wetting length curve, aiming to minimize the residual between the model-predicted wetting length and the measured wetting length, optimizing the swelling and shrinkage coefficient, the critical length for complete saturation, the initial tortuosity damping factor, and related indices. For example, seepage evolution data can be fitted under normal temperature and high temperature conditions respectively to obtain the parameters of the temperature field empirical model and establish the temperature field empirical model.

[0125] The initial effective radius, swelling and shrinkage coefficient, and critical length for complete saturation are used to determine the dynamic effective pore size. The initial effective radius is the baseline equivalent pore size of the storage cotton in the state of no oil absorption when the wetting length approaches zero, denoted as r0. The swelling and shrinkage coefficient is a coefficient characterizing the degree of shrinkage of the effective pore size of the porous channel after the fiber absorbs the target liquid, denoted as ηs, and can be a calibration value in the range of 0.05 to 0.3. The critical length for complete saturation is the limit value of the wetting length corresponding to the complete saturation state of the storage cotton, denoted as Lmax. The definition and function of the dynamic effective pore size are as described in the previous method embodiment. It is a parameter characterizing the effective pore size of the porous channel after the storage cotton absorbs the target liquid and swells. The dynamic effective pore size is expressed as reff(L)=r0·[1 ηs·(L / Lmax)^m], where m is the swelling nonlinear exponent.

[0126] For example, in the initial stage of seepage (when the wetting length is less than one-fifth of the critical length for complete saturation), it can be approximated as Newtonian isothermal seepage, and the initial value of the ratio of the initial effective radius to the reference viscosity can be obtained by linear regression of the square of the wetting length against time. For example, nonlinear least squares fitting can be performed on the complete wetting length curve, with the goal of minimizing the residual between the model-predicted wetting length and the measured wetting length, while simultaneously optimizing the swelling-shrinkage coefficient, the critical length for complete saturation, and the swelling nonlinearity index.

[0127] The initial tortuosity damping factor is used to determine the tortuosity factor. The initial tortuosity damping factor is the tortuosity damping value of the liquid storage cotton under dry initial conditions, denoted as τ2,0, and can take a typical value in the range of 0.62 to 0.85. The definition and function of the tortuosity factor are as described in the previous method embodiment. It is a parameter characterizing the damping effect of the path tortuosity of the porous channel of the liquid storage cotton on the driving pressure difference. The tortuosity factor is expressed as τ2(S)=τ2,0·[1 [α·S^β], where α is the saturation sensitivity coefficient and can take a typical value in the range of 0.15 to 0.35, β is the nonlinear exponent and can take a typical value in the range of 1.5 to 2.5, and S is the local saturation and is approximately the wetting length divided by the critical length for complete saturation.

[0128] That is, the initial tortuosity damping factor obtained from the inversion is used as the benchmark term of the tortuosity factor formula. Combined with local saturation, the tortuosity factor at the current position is calculated. The role of this initial tortuosity damping factor is to provide a dynamic correction for the path curvature side of the capillary flow rate determination model, correcting the classical model's assumption that the flow path is treated as a straight pipe. For example, when performing nonlinear least-squares fitting on the complete immersion length curve, the initial tortuosity damping factor, along with the saturation sensitivity coefficient and the nonlinear exponent, can be solved as optimization variables. Alternatively, based on the porosity and fiber orientation characteristics of the liquid storage cotton, an initial guess value of the initial tortuosity damping factor can be estimated using an empirical correlation formula, and then fine-tuned through fitting.

[0129] The empirical temperature field model parameters are used to determine the local temperature field, which in turn determines the dynamic surface tension and composite dynamic viscosity. These parameters are empirical formula coefficients describing the temperature distribution within the liquid storage cotton caused by heat transfer from the heating element. They may include the maximum temperature rise near the heating element ΔTmax, the temperature decay characteristic coefficient λ, and the heating time constant τh. The temperature field is expressed as T(L,t) = T0 + ΔT(t)·exp( λL), where the temperature rise amplitude ΔT(t) = ΔTmax·[1 exp( [t / τh)], where T0 is the baseline ambient temperature at the far end of the liquid storage cotton. The definition and function of the local temperature field are as described in the previous method embodiment. It is a temperature distribution function at various locations along the wetting length inside the liquid storage cotton. This distribution decays exponentially along the wetting length under the temperature boundary conditions near the heat source. It transforms the internal temperature distribution of the liquid storage cotton, which cannot be directly measured, into a continuous field that can be estimated by software algorithms, providing temperature input for determining composite dynamic viscosity and dynamic surface tension.

[0130] In other words, the parameters of the empirical temperature field model obtained from the inversion are substituted into the temperature field formula, and the processor estimates the local temperature field. The local temperature field is then substituted into the composite dynamic viscosity formula and the dynamic surface tension formula to update the corresponding parameters. The role of the aforementioned empirical temperature field model parameters is to establish a software calculation link from the heating time of the heating element to the correction of fluid properties, enabling the model to incorporate the influence of time-varying temperature fields without sensors. For example, seepage evolution data can be collected under both normal and high-temperature conditions. By comparing the differences in the advancement rate of the wetting length under the two conditions, the maximum temperature rise amplitude near the heating element, the temperature decay characteristic coefficient, and the heating time constant can be obtained through fitting. Alternatively, infrared thermal imaging equipment can be used to perform offline temperature calibration on the same batch of liquid storage cotton, and the temperature decay characteristic coefficient can be extracted and solidified into the empirical temperature field model. Furthermore, the empirical temperature field model parameters can be organized into a lookup table in the processor, and the temperature rise amplitude can be calculated in real time according to the heating time of the heating element, with exponential decay along the wetting length direction to obtain the local temperature field.

[0131] By calibrating the basic physical properties of the target liquid in one go through standard rheological and interface tests, and by inverting the parameters of the liquid storage cotton medium and temperature field parameters from the seepage evolution data, all parameters of the capillary seepage oil conduction rate determination model can be obtained without adding temperature sensors or pore probes. This enables the model to have engineering adaptability for specific combinations of liquid storage cotton and target liquid, reducing the errors of the classic Lucas-Washburn model from 19.7% under normal temperature conditions, 37.2% under high temperature conditions, and 45.6% under high saturation conditions to 3.1% to 5.8% in this real-time example, thus improving the engineering feasibility and calculation accuracy of the liquid storage cotton oil conduction rate determination method.

[0132] Please refer to Figure 5 As shown, Figure 5 A schematic flowchart of a method for determining the oil conduction rate of a liquid storage cotton provided in this disclosure is shown. An embodiment of this disclosure also provides an apparatus 500 for determining the oil conduction rate of a liquid storage cotton, comprising: The data acquisition component 501 is used to acquire seepage evolution data during the seepage process. The seepage evolution data is used to characterize the evolution relationship between the wetting length of the liquid storage cotton under the action of capillary driving force and the acquisition time. The processor 502, connected to the data acquisition component 501, is used to determine the instantaneous oil conduction rate of the liquid storage cotton based on the capillary seepage oil conduction rate determination model and seepage evolution data. Among them, the capillary flow oil conduction rate determination model is used to characterize the physical relationship between immersion length, immersion time and instantaneous oil conduction rate, and is obtained by product coupling dynamic correction of dynamic effective pore size, composite dynamic viscosity, dynamic surface tension and tortuosity factor; dynamic effective pore size is used to characterize the effective pore size of the porous channels of the liquid storage cotton after it absorbs the target liquid and swells; composite dynamic viscosity is used to characterize the apparent viscosity of the target liquid as a function of shear rate and temperature; dynamic surface tension is used to characterize the thermal degradation characteristics of the target liquid; tortuosity factor is used to characterize the damping effect of the path tortuosity of the porous channels of the liquid storage cotton on the driving pressure difference.

[0133] This disclosure provides a device for determining the oil conduction rate of a liquid-absorbing cotton. The device may include a data acquisition component and a processor. These components work together to execute the aforementioned method for determining the oil conduction rate of the liquid-absorbing cotton, enabling a fully differential physical reconstruction of the classical Lucas-Washburn model without altering the existing hardware measurement system. The problem addressed by this device is that the classical Lucas-Washburn model, based on an ideal straight-tube capillary model, assumes constant pore size, constant viscosity, no temperature gradient, and a Newtonian fluid. This leads to severe distortion in electronic heaters under actual operating conditions (porous, multiphase, non-isothermal, non-Newtonian flow). This device, through the collaboration of hardware acquisition and software algorithms, transforms complex and unmeasurable variables into independent variables that can be calculated by software using the immersion length and acquisition time, thereby outputting the instantaneous oil conduction rate at any given moment.

[0134] The reservoir cotton can be the oil-wicking cotton used in electronic heaters, such as polyethylene terephthalate and polyamide 6 integrated cotton, with a density of 0.085 g / cm³ and a porosity of 95%. The target liquid can be the liquid to be heated, such as a glycerol-propylene glycol mixture with a density of 1.104 g / cm³ and a viscosity of 130.4 mPa·s (25°C). The capillary driving force is the pressure difference generated by surface tension in the porous channels of the reservoir cotton for the target liquid. The magnitude of this driving force is determined at least by the dynamic surface tension of the target liquid and the geometric characteristics of the porous channels of the reservoir cotton. Its physical expression can be written as the capillary pressure difference equal to the product of four times the dynamic surface tension and the cosine of the contact angle divided by the effective pore size of the porous channels.

[0135] Longitudinal seepage refers to the one-dimensional propagation of the target liquid along the length of the storage cotton. The purpose of the testing fixture is to provide a standardized physical testing environment for the storage cotton, allowing the target liquid to undergo capillary seepage under controlled conditions, thereby providing an observable seepage process for the data acquisition components. For example, during testing, the testing fixture can be designed as a vertical clamping structure, with the upper end of the storage cotton fixed to the clamp and the lower end immersed below the liquid surface of the container containing the target liquid, utilizing capillary effect to allow the target liquid to seep longitudinally along the storage cotton from bottom to top. For example, the testing fixture can also be designed as a horizontal clamping structure, with one end of the storage cotton contacting the target liquid pool, allowing the target liquid to wet horizontally. For example, the testing fixture can also integrate a temperature control module to provide a comparative testing environment under normal and high-temperature conditions during the calibration phase, in order to establish an empirical model of the temperature field.

[0136] The data acquisition component is used to acquire seepage evolution data during the seepage process. This seepage evolution data characterizes the relationship between the wetting length of the liquid-storing cotton under capillary driving force and the acquisition time. The data acquisition component is a set of hardware devices that acquires the original signals of the seepage process of the liquid-storing cotton at preset sampling intervals. It may include at least one of imaging devices and weighing sensors, and is equipped with analog-to-digital conversion and communication interfaces. It transforms the internal seepage process, which cannot be directly observed, into externally measurable signals that can be processed by a processor, providing basic input for the capillary seepage oil conduction rate determination model. This acquisition process relies on the collaboration of physical sensing devices and processors, constituting the data acquisition means in the technical solution rather than abstract data recording.

[0137] The definition and function of seepage evolution data are as described in the preceding method embodiments. It is a dataset characterizing the evolutionary relationship between the wetting length and the acquisition time. For example, the data acquisition component can continuously acquire longitudinal images of the liquid-storing cotton using a high-speed camera at a preset sampling interval, which can be set to 0.5 s. The wetting length corresponding to each acquisition time is obtained by identifying the liquid front position from the longitudinal images. Alternatively, the data acquisition component can continuously acquire the liquid mass of the liquid-storing cotton using a weighing sensor at a preset sampling interval, and obtain the wetting length corresponding to each acquisition time based on the conversion relationship between mass and volume. The target liquid density can be 1.104 g / cm³. Furthermore, the data acquisition component can also simultaneously include a high-speed camera and a weighing sensor, acquiring longitudinal images and liquid mass at the same preset sampling interval to form a dual-modal raw dataset for processor cross-validation.

[0138] The processor is connected to the data acquisition component and is used to determine the instantaneous oil conduction rate of the storage cotton based on the capillary seepage oil conduction rate determination model and seepage evolution data. The processor is a hardware processing unit with data processing capabilities. It can establish a wired or wireless communication connection with the data acquisition component to receive seepage evolution data. The processor can store computer-executable instructions. When the instructions are executed by the processor, the step of determining the instantaneous oil conduction rate based on the capillary seepage oil conduction rate determination model mentioned in the previous method embodiment is implemented. The processor's function is to invoke the capillary seepage oil conduction rate determination model, perform multi-physics field coupling correction and differential equation solving on the seepage evolution data provided by the data acquisition component, and output the instantaneous oil conduction rate at any given time. This processing is a means to solve the technical problem of inaccurate oil conduction rate measurement under real working conditions, making the entire device a patentable technical solution rather than a simple mathematical calculation device.

[0139] For example, the processor can read the current wetting length corresponding to the current acquisition time in the seepage evolution data, substitute the current wetting length into the capillary seepage oil conduction rate determination model, use the instantaneous oil conduction rate as the unknown quantity and the instantaneous oil conduction rate at the previous acquisition time as the initial value, and adaptively update the instantaneous oil conduction rate through the fixed-point iteration method or the Newton iteration method until the relative deviation value of two adjacent iteration results is less than a preset deviation threshold. The preset deviation threshold can be 10. -4 The processor can also use the time interval of the seepage evolution data as a reference to recursively calculate the step size, and perform time-series recursion to obtain the seepage length corresponding to the next acquisition time. If the rate of change of the instantaneous oil conduction rate at the current acquisition time relative to the previous acquisition time exceeds a preset rate of change threshold, the reference recursion step size is reduced to the target recursion step size and the recursion is restarted. For example, the processor can also use the calculated instantaneous oil conduction rate to monitor the oil conduction status of the heating component or output the selection evaluation results of the liquid storage cotton, corresponding to various application implementations in the preceding method embodiments.

[0140] A capillary flow oil conduction rate determination model is used to characterize the physical relationship between wetting length, wetting time, and instantaneous oil conduction rate. This model is obtained through dynamic correction via product coupling of dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor. This model can be stored in the processor's memory as computer-executable instructions and executed by the processor. The definitions and physical meanings of dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor are as described in the previous method embodiments: dynamic effective pore size is a parameter characterizing the effective pore size of the porous channels of the liquid storage cotton after it absorbs and swells from the target liquid; composite dynamic viscosity is a parameter characterizing the apparent viscosity of the target liquid as a function of shear rate and temperature; dynamic surface tension is a parameter characterizing the thermal degradation characteristics of the target liquid; and tortuosity factor is a parameter characterizing the damping effect of the path tortuosity of the porous channels of the liquid storage cotton on the driving pressure difference.

[0141] The above four factors, together with the product-coupled dynamic correction executed by the processor, constitute the core model parameters of the capillary flow oil conduction rate determination model. The idealized constant assumptions in the classical Lucas-Washburn model are replaced with dynamic variables that conform to actual operating conditions, so that the instantaneous oil conduction rate calculated by the processor can reflect the real flow state of non-Newtonian, non-isothermal, porous and multiphase flow. This reduces the error of the classical Lucas-Washburn model from 19.7% under normal temperature conditions and 37.2% under high temperature conditions to 3.1% to 5.8% in this real-time example.

[0142] For example, the processor can dynamically adjust the effective aperture reff(L) = r0·[1] ηs·(L / Lmax)^m]、Complex dynamic viscosity ηdynamic=K·γ^(n 1)·exp[B / (T T∞)]、Dynamic surface tension γ(T)=γ0·[1 δ·ΔT(t)·exp( λL)] and tortuosity factor τ2(S)=τ2,0·[1 Substituting α·S^β] into the seepage velocity formula using a product coupling method, the governing equation is reconstructed as V=reff(L)·[γ(T)·cosθ·τ2(S) / (4·K·L·exp[B / (T)]]. T∞)])]^(1 / n), where θ is the contact angle. For example, the processor can also estimate the local temperature field T(L,t)=T0+ΔT(t)·exp( ) under sensorless conditions based on the heating time of the heat source. λL), and based on this, the composite dynamic viscosity and dynamic surface tension are determined and written back into the model. The temperature decay characteristic coefficient λ can be obtained by calibrating the temperature distribution of the same batch of liquid storage cotton.

[0143] By clamping the liquid-containing cotton with a test fixture and inducing longitudinal seepage, the data acquisition component obtains seepage evolution data at preset sampling intervals. Then, the processor connected to the data acquisition component performs product-coupled dynamic correction and solution based on the capillary seepage oil conduction rate determination model. This can achieve accurate quantification of the oil conduction rate without changing the existing hardware measurement system. The error of the classic Lucas-Washburn model is reduced from 19.7% under normal temperature conditions, 37.2% under high temperature conditions, and 45.6% under high saturation conditions to 3.1% to 5.8% in this real-time example, improving the accuracy and engineering applicability of the electronic heater in determining the oil conduction rate of the liquid-containing cotton.

[0144] As one implementation, the data acquisition components include an imaging device and / or a weighing sensor, wherein: An imaging device, connected to a processor, is used to acquire longitudinal images of a liquid storage cotton at preset sampling intervals during the process of the target liquid seeping longitudinally along the storage cotton under capillary driving force. A weighing sensor, connected to a processor, is used to collect the liquid mass of the storage cotton at preset sampling intervals during the process of the target liquid seeping longitudinally along the storage cotton under capillary driving force. The processor is used to determine the immersion length corresponding to each acquisition time based on the longitudinal image and / or liquid mass; and to determine the seepage evolution data based on the immersion length corresponding to each acquisition time.

[0145] As one implementation method, the imaging device can be a high-speed camera, and the weighing sensing device can be a weighing sensor. Both the high-speed camera and the weighing sensor can establish a communication connection with the processor. The definition and function of the data acquisition component are as described in the previous device embodiment. It is a set of hardware devices that collect the original signals of the seepage process of the liquid storage cotton at preset sampling intervals. In this implementation method, this set is specifically manifested as two physical acquisition modes: the imaging path and the weighing path.

[0146] A high-speed camera connected to a processor is used to acquire longitudinal images of the storage cotton at preset sampling intervals during the longitudinal seepage of the target liquid along the storage cotton under capillary driving force. The high-speed camera is an imaging device that captures image frames from the side of the storage cotton using optical imaging principles. It can connect to the processor via a universal serial bus interface, Ethernet interface, or wireless transmission module, sending the acquired image data to the processor in real time. The definition and function of the longitudinal image are as described in the previous method embodiment; it is an image frame containing the position of the liquid wetting front, where the target liquid wetting area and the dry area form a recognizable boundary. The preset sampling interval is defined as described above, specifically a fixed time interval for the data acquisition component to acquire raw data, which can be set to 0.5s. This means that during the actual physical process of the storage cotton being clamped in the test fixture and the target liquid undergoing longitudinal seepage, the high-speed camera continuously captures images of the side of the storage cotton at fixed time intervals and transmits the image frames to the processor. This transforms the internal seepage process, which cannot be directly observed, into an external visual signal that can be processed by the processor's image algorithm, providing a data source for subsequent wetting length extraction. This acquisition process relies on the hardware connection and data path between the physical imaging device and the processor.

[0147] For example, a high-speed camera can be positioned directly in front of the liquid-absorbing cotton on the side, capturing longitudinal images of the cotton at a preset sampling interval of 0.5 seconds. The image resolution can be set to pixel specifications that meet the accuracy requirements for liquid front recognition. Alternatively, the high-speed camera can be equipped with a backlight source to create a clear grayscale contrast between the target liquid-wetted area and the dry area in the cotton, thereby improving the robustness of liquid front recognition in the longitudinal images. Furthermore, a timestamp can be added synchronously each time a longitudinal image is captured by the high-speed camera, ensuring a one-to-one correspondence between image frames and capture times, facilitating the processing of seepage evolution data.

[0148] A weighing sensor connected to a processor is used to collect the liquid mass of the storage cotton at preset sampling intervals during the longitudinal seepage of the target liquid along the storage cotton under capillary driving force. The weighing sensor is a sensing device that uses strain gauges or a balancing mechanism to measure mass or mass change. It can be positioned below the test fixture or integrated into the fixture's clamps, and is connected to the processor via a signal line to transmit mass readings. The liquid mass is defined as described in the previous method embodiments: it is the total mass or mass change of the storage cotton and the adsorbed target liquid, measured by the weighing sensor.

[0149] During the longitudinal seepage of the liquid-storing cotton, a load cell continuously measures the mass of the cotton at the same preset sampling interval as the high-speed camera, and transmits the mass data to the processor. This converts the amount of liquid absorbed during seepage into a quantifiable mass signal, providing a data source for subsequent calculation of the wetting length. This acquisition process also relies on the hardware connection between the physical sensing device and the processor. For example, the load cell can be positioned below the container holding the target liquid, collecting the total mass of the cotton and the absorbed liquid at a preset sampling interval of 0.5 seconds, and obtaining the liquid mass increment at each moment by subtracting it from the initial dry mass. Alternatively, the load cell can directly measure the mass change of the cotton while it is suspended, outputting the mass change as the liquid mass. Furthermore, the load cell can also include a sampling time marker when outputting the liquid mass, aligning the mass reading with the sampling time for the processor to assemble the seepage evolution data.

[0150] The processor invokes image processing algorithms or mass conversion algorithms to convert the longitudinal image transmitted by the high-speed camera and / or the liquid mass transmitted by the weighing sensor into a wetting length value corresponding to each acquisition time. Then, it organizes the acquisition time identifier and its corresponding wetting length value into an ordered data structure to generate seepage evolution data, unifying the heterogeneous hardware sensor data into standardized geometric parameters and time series. The definition and function of the wetting length corresponding to each acquisition time are as described in the previous method embodiment; it is the wetting length value corresponding to the acquisition time in the seepage evolution data. The definition and function of the seepage evolution data are as described above; it is a data set characterizing the evolutionary relationship between the wetting length and the acquisition time.

[0151] For example, for a vertical image, the processor can perform edge detection or thresholding to identify the pixel coordinates of the liquid front, and then convert the pixel coordinates into the wetting length according to the calibrated pixel physical ratio. That is, the wetting length is equal to the pixel position of the liquid front multiplied by the physical size of a single pixel. For example, for liquid mass, the processor can divide the mass increment by the target liquid density to obtain the liquid volume, and then divide the liquid volume by the equivalent cross-sectional area of ​​the liquid storage cotton to obtain the wetting length. That is, the wetting length is equal to the liquid mass increment divided by the product of the target liquid density and the cross-sectional area of ​​the liquid storage cotton, where the target liquid density can be 1.104 g / cm³.

[0152] For example, for bimodal data containing both longitudinal images and liquid mass, the processor can extract the seepage length from the longitudinal image and calculate the seepage length from the liquid mass, respectively, and then output the seepage length corresponding to each acquisition time after performing a weighted average or consistency check. For example, the processor can organize the seepage lengths corresponding to each acquisition time into a time-sorted key-value pair sequence, where each key-value pair contains an acquisition time identifier and the corresponding seepage length value; this sequence is the seepage evolution data. Alternatively, the processor can fit each acquisition time and seepage length into a discrete function table with the acquisition time as the independent variable and the seepage length as the dependent variable.

[0153] The longitudinal image and liquid mass of the liquid storage cotton are acquired at preset sampling intervals using a high-speed camera and / or a weighing sensor. The processor determines the wetting length corresponding to each acquisition moment based on the longitudinal image and / or liquid mass to form seepage evolution data. The hardware acquisition capability of the existing test fixture can be combined with the processor software algorithm to obtain the evolution sequence of the wetting length and acquisition moment required for the capillary seepage oil conduction rate determination model without adding a temperature sensor or pore probe. This provides aligned time grid and geometric input for subsequent single-point implicit iterative solution of instantaneous oil conduction rate and step-size adaptive time-series recursion, thereby realizing the physical signal acquisition and data preprocessing functions of the entire liquid storage cotton oil conduction rate determination device.

[0154] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this disclosure.

[0155] Corresponding to the method in the above embodiments, Figure 6 This is a schematic diagram of a device for determining the oil conduction rate of a liquid storage cotton according to an embodiment of this disclosure. This device can be implemented by software, hardware, or a combination of both, forming part or all of a computer device. This computer device can be... Figure 7 The electronic device shown.

[0156] Reference Figure 6 The oil conduction rate determining device 600 for the liquid storage cotton includes: The acquisition unit 601 is used to acquire seepage evolution data of the liquid storage cotton during the seepage process. The seepage process is used to characterize the longitudinal seepage of the target liquid along the liquid storage cotton due to capillary driving force. The seepage evolution data is used to characterize the corresponding evolution relationship between the wetting length of the liquid storage cotton under the action of capillary driving force and the acquisition time. The determining unit 602 is used to determine the instantaneous oil conduction rate of the liquid storage cotton based on the capillary seepage oil conduction rate determination model and seepage evolution data. Among them, the capillary flow oil conduction rate determination model is used to characterize the physical relationship between immersion length, immersion time and instantaneous oil conduction rate, and is obtained by product coupling dynamic correction of dynamic effective pore size, composite dynamic viscosity, dynamic surface tension and tortuosity factor; dynamic effective pore size is used to characterize the effective pore size of the porous channels of the liquid storage cotton after it absorbs the target liquid and swells; composite dynamic viscosity is used to characterize the apparent viscosity of the target liquid as a function of shear rate and temperature; dynamic surface tension is used to characterize the thermal degradation characteristics of the target liquid; tortuosity factor is used to characterize the damping effect of the path tortuosity of the porous channels of the liquid storage cotton on the driving pressure difference.

[0157] It is understood that the embodiments of the oil conduction rate determination device for the liquid storage cotton and any implementation thereof correspond to the embodiments of the oil conduction rate determination method for the liquid storage cotton and any implementation thereof. The technical effects corresponding to the embodiments of the oil conduction rate determination device for the liquid storage cotton and any implementation thereof can be found in the above-mentioned technical effects corresponding to the embodiments of the oil conduction rate determination method for the liquid storage cotton and any implementation thereof, and will not be repeated here.

[0158] It should be noted that the oil conduction rate determination device for the liquid storage cotton provided in the above embodiments is only an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0159] The functional units and modules in the above embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of the embodiments of this disclosure.

[0160] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this disclosure. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0161] This disclosure also provides an electronic device, which includes one or more processors and a memory; The memory is coupled to one or more processors. The memory is used to store computer program code, which includes computer instructions. The one or more processors call the computer instructions to cause the electronic device to execute the aforementioned method for determining the oil conduction rate of the liquid storage cotton.

[0162] Figure 7This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. The electronic device 700 can be a mobile phone, smart screen, tablet computer, wearable electronic device, in-vehicle electronic device, augmented reality (AR) device, virtual reality (VR) device, laptop computer, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), projector, or a communication device such as a server, storage device, or base station, or a smart car, etc. This disclosure does not limit the specific type of electronic device.

[0163] The memory 701 can be used to store computer programs 702 and modules. The processor 703 executes various functional applications and data processing of the electronic device by running the software programs and modules stored in the memory 701. The memory 701 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device (such as audio data, telephone directory, etc.). In addition, the memory 701 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0164] The processor 703 may include one or more processors such as a central processing unit (CPU), an application processor (AP), and a baseband processor. The processor can serve as the nerve center and command center of the wireless router. The processor 703 can generate operation control signals based on instruction opcodes and timing signals to control instruction fetching and execution. The memory 701 can be used to store executable program code, including instructions. The processor 703 executes various functional applications and data processing of the network device by running the instructions stored in the memory. The memory 701 may include a program storage area and a data storage area, such as storing data for audio signals to be played. For example, the memory may be Double Data Rate Synchronous Dynamic Random Access Memory (DDR) or Flash memory.

[0165] This disclosure also provides a computer-readable storage medium storing computer instructions; when the computer-readable storage medium is used on an electronic device, the electronic device performs the aforementioned method for determining the oil conduction rate of the liquid storage cotton.

[0166] 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 via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) 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 it can include one or more data storage devices such as servers or data centers that can be integrated with media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media, or semiconductor media (e.g., solid-state disks (SSDs)).

[0167] This disclosure also provides a computer program product containing computer instructions, which, when run on an electronic device, enables the electronic device to execute the aforementioned method for determining the oil conduction rate of the liquid storage cotton.

[0168] The computer storage medium and computer program product provided in the above-described embodiments are used to execute the methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects corresponding to the methods provided above, and will not be repeated here.

[0169] In the above embodiments, implementation can also be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this disclosure 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 via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer, or a data storage device such as a server or data center that integrates one or more available media. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc., and the storage medium can also include combinations of the above types of memory.

[0170] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0171] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments claimed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0172] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0173] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0174] The technical features of the various embodiments described above in this disclosure can be combined arbitrarily without conflict. For the sake of brevity, this specification does not describe all possible combinations, but as long as these combinations do not violate the technical spirit of this disclosure, they should all be considered within the scope of this disclosure. Based on the content disclosed in this disclosure, those skilled in the art can reasonably combine, delete, or replace the technical features of the above embodiments according to actual needs, and such modifications and variations all fall within the protection scope of this disclosure.

[0175] The above-described embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit it. Although this disclosure 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 spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be included within the protection scope of this disclosure.

Claims

1. A method for determining the oil conduction rate of a liquid storage cotton, characterized in that, The method includes: The seepage evolution data of the liquid storage cotton during the seepage process is obtained. The seepage process is used to characterize the longitudinal seepage of the target liquid along the liquid storage cotton due to capillary driving force. The seepage evolution data is used to characterize the corresponding evolution relationship between the wetting length of the liquid storage cotton under the action of capillary driving force and the collection time. Based on the capillary seepage oil conduction rate determination model, the instantaneous oil conduction rate of the liquid storage cotton is determined according to the seepage evolution data; The capillary flow oil conduction rate determination model is used to characterize the physical relationship between immersion length, immersion time, and instantaneous oil conduction rate, and is obtained by product coupling dynamic correction of dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor. The dynamic effective pore size is used to characterize the effective pore size of the porous channels of the liquid storage cotton after it absorbs and swells with the target liquid. The composite dynamic viscosity is used to characterize the apparent viscosity of the target liquid as a function of shear rate and temperature. The dynamic surface tension is used to characterize the thermal degradation characteristics of the target liquid. The tortuosity factor is used to characterize the damping effect of the path tortuosity of the porous channels of the liquid storage cotton on the driving pressure difference.

2. The method according to claim 1, characterized in that, After determining the instantaneous oil conduction rate of the liquid storage cotton based on the capillary seepage oil conduction rate determination model and the seepage evolution data, the method further includes: Based on the instantaneous oil conduction rate, determine at least one of the following: cumulative liquid conduction volume, saturation evolution curve, and average oil conduction rate of the liquid storage cotton; Based on at least one of the cumulative liquid conduction volume, the saturation evolution curve, and the average oil conduction rate, and compared with the corresponding preset selection threshold, the selection evaluation result of the liquid storage cotton is output.

3. The method according to claim 1, characterized in that, The capillary seepage oil conduction rate determination model determines the instantaneous oil conduction rate of the liquid storage cotton based on the seepage evolution data, including: Determine the current infiltration length corresponding to the current acquisition time from the seepage evolution data; The current immersion length is substituted into the capillary flow oil conduction rate determination model. The instantaneous oil conduction rate is taken as an unknown quantity, and the determined instantaneous oil conduction rate at the previous acquisition time is taken as an initial value. The instantaneous oil conduction rate is adaptively updated by the fixed-point iteration method or the Newton iteration method until the relative deviation between two adjacent iterations is less than a preset deviation threshold. The capillary flow oil conduction rate determination model is associated with the dynamic effective pore size, the composite dynamic viscosity, the dynamic surface tension, the tortuosity factor, and the instantaneous oil conduction rate in a product coupling manner.

4. The method according to claim 3, characterized in that, After adaptively updating the instantaneous oil transfer rate using a fixed-point iteration method or a Newton-Raphson iteration method, the method further includes: Using the time interval of the seepage evolution data as a reference, the step size is recursively calculated, and the model is determined according to the capillary seepage oil conduction rate to perform time-series recursion on the wetting length, so as to obtain the preliminary wetting length corresponding to the next collection time. If the rate of change of the instantaneous oil guiding rate at the current acquisition time relative to the instantaneous oil guiding rate at the previous acquisition time exceeds a preset rate of change threshold, the reference recursive step size is reduced to the target recursive step size, and the immersion length is re-time-recursively recursed using the target recursive step size to obtain the immersion length corresponding to the next acquisition time after correction. The immersion length corresponding to the next sampling moment after the correction is used as the basis for determining the instantaneous oil conduction rate corresponding to the next sampling moment.

5. The method according to claim 1, characterized in that, The step of obtaining the seepage evolution data of the liquid storage cotton during the seepage process includes: During the process of the target liquid seeping longitudinally along the liquid storage cotton due to the capillary driving force, the longitudinal image of the liquid storage cotton is acquired by the data acquisition component at a preset sampling interval; and / or, the liquid mass of the liquid storage cotton is acquired at a preset sampling interval. Based on the longitudinal image and / or the liquid mass, determine the immersion length corresponding to each acquisition time. The seepage evolution data are determined based on the infiltration length corresponding to each collection time.

6. The method according to any one of claims 1 to 5, characterized in that, The liquid-storing cotton is used in the heating assembly of an electronic heater, the heating assembly further includes a heating element, and the method further includes: The heating time of the heating element in the heating assembly is obtained, and the end where the heating element is located is taken as the heat source near end; The temperature rise amplitude near the heat source is determined based on the heating time, wherein the temperature rise amplitude changes dynamically with the heating time. Using the temperature rise amplitude as the temperature boundary near the heat source, the local temperature field is estimated along the wetting length of the liquid storage cotton in an exponential decay manner, wherein the local temperature field decays exponentially with the increase of the wetting length. Based on the local temperature field, the composite dynamic viscosity and dynamic surface tension in the capillary flow oil conduction rate determination model are determined, wherein the composite dynamic viscosity changes with the temperature in the local temperature field, and the dynamic surface tension thermally degrades with the temperature in the local temperature field.

7. The method according to any one of claims 1 to 5, characterized in that, The capillary flow oil conduction rate determination model is constructed in the following manner: An ideal straight pipe reference model is established, which simplifies the porous channel of the liquid storage cotton into a rigid, constant-diameter, straight capillary, and follows the assumption of a straight path in the classical capillary flow theory that the fluid properties and pipe characteristics are constant. A dynamic correction factor is determined, wherein the dynamic correction factor includes: dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor, and the composite dynamic viscosity is obtained by coupling non-Newtonian power-law viscosity and temperature viscosity dependence. Non-Newtonian power-law viscosity is used to characterize the rheological properties of the target liquid under high shear rate, and temperature viscosity dependence is used to characterize the thermal thinning properties of the target liquid whose viscosity decreases exponentially with increasing temperature. The dynamic effective pore size, the composite dynamic viscosity, the dynamic surface tension, and the tortuosity factor are respectively replaced by the constant pore size, constant viscosity, constant surface tension, and straight path assumption in the ideal straight pipe reference model as corresponding replacement targets, and then substituted into the ideal straight pipe reference model in a product coupling manner to obtain the capillary seepage oil conduction rate determination model.

8. The method according to claim 1, characterized in that, The liquid-retaining cotton is used in the heating assembly of an electronic heater. After determining the instantaneous oil conduction rate of the liquid-retaining cotton based on the capillary seepage oil conduction rate determination model and the seepage evolution data, the method further includes: Based on the instantaneous oil conduction rate, the oil conduction status of the heating component is monitored, wherein when the instantaneous oil conduction rate is lower than a preset rate threshold, the oil conduction status is a dry-burning risk state; In response to the oil guiding state being a dry-burning risk state, a control command is output. The control command is used to control the heating component to reduce the heating power to prevent the heating component from dry-burning.

9. A device for determining the oil conduction rate of a liquid storage cotton, characterized in that, The liquid-storing cotton is used in the heating assembly of an electronic heater, and the device includes: A data acquisition component is used to acquire the wetting length of the liquid storage cotton during the seepage process, and the seepage process is used to characterize the longitudinal seepage of the target liquid along the liquid storage cotton due to capillary driving force. The processor, connected to the data acquisition component, is used to acquire seepage evolution data of the liquid storage cotton during the seepage process. The seepage evolution data is used to characterize the evolution relationship between the immersion length of the liquid storage cotton under the action of capillary driving force and the acquisition time. Based on the capillary seepage oil conduction rate determination model, the instantaneous oil conduction rate of the liquid storage cotton is determined according to the seepage evolution data. The capillary flow oil conduction rate determination model is used to characterize the physical relationship between immersion length, immersion time, and instantaneous oil conduction rate, and is obtained by product coupling dynamic correction of dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor. The dynamic effective pore size is used to characterize the effective pore size of the porous channels of the liquid storage cotton after it absorbs and swells with the target liquid. The composite dynamic viscosity is used to characterize the apparent viscosity of the target liquid as a function of shear rate and temperature. The dynamic surface tension is used to characterize the thermal degradation characteristics of the target liquid. The tortuosity factor is used to characterize the damping effect of the path tortuosity of the porous channels of the liquid storage cotton on the driving pressure difference.

10. The apparatus according to claim 9, characterized in that, The data acquisition component includes an imaging device and / or a weighing sensor, wherein: The imaging device, connected to the processor, is used to acquire longitudinal images of the liquid storage cotton at preset sampling intervals during the process of the target liquid seeping longitudinally along the liquid storage cotton under capillary driving force. The weighing sensor is connected to the processor and is used to collect the liquid mass of the liquid storage cotton at preset sampling intervals during the process of the target liquid seeping longitudinally along the liquid storage cotton under capillary driving force. The processor is configured to determine the immersion length corresponding to each acquisition time based on the longitudinal image and / or the liquid mass; and to determine the seepage evolution data based on the immersion length corresponding to each acquisition time.

11. A device for determining the oil conduction rate of a liquid storage cotton, characterized in that, The device includes: The acquisition unit is used to acquire seepage evolution data of the liquid storage cotton during the seepage process. The seepage process is used to characterize the longitudinal seepage of the target liquid along the liquid storage cotton due to capillary driving force. The seepage evolution data is used to characterize the corresponding evolution relationship between the wetting length of the liquid storage cotton under the action of capillary driving force and the acquisition time. The determining unit is used to determine the instantaneous oil conduction rate of the liquid storage cotton based on the capillary seepage oil conduction rate determination model and the seepage evolution data. The capillary flow oil conduction rate determination model is used to characterize the physical relationship between immersion length, immersion time, and instantaneous oil conduction rate, and is obtained by product coupling dynamic correction of dynamic effective pore size, composite dynamic viscosity, dynamic surface tension, and tortuosity factor. The dynamic effective pore size is used to characterize the effective pore size of the porous channels of the liquid storage cotton after it absorbs and swells with the target liquid. The composite dynamic viscosity is used to characterize the apparent viscosity of the target liquid as a function of shear rate and temperature. The dynamic surface tension is used to characterize the thermal degradation characteristics of the target liquid. The tortuosity factor is used to characterize the damping effect of the path tortuosity of the porous channels of the liquid storage cotton on the driving pressure difference.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it causes the electronic device to implement the method as described in any one of claims 1 to 8.

13. A computer program product, characterized in that, Includes a computer program, which, when run, causes the method as described in any one of claims 1 to 8 to be performed.

14. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 8.

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