Method and system for evaluating oxidation deterioration of biofuel based on conductivity characteristic analysis

Abstract

The present application relates to the technical field of biofuel monitoring, and particularly relates to a biofuel oxidation deterioration evaluation method and system based on conductivity characteristic analysis. The method comprises the following steps: collecting real-time conductivity and temperature data of biofuel in a storage tank and filtering; calculating the absolute value of the difference between the actual increment and the theoretical increment caused only by temperature change based on a preset temperature compensation coefficient to obtain a non-thermal change rate residual; constructing a sliding window to calculate the standard deviation of the residual, and combining the current conductivity to obtain a logarithmic amplification to obtain an oxidation microscopic instability index; constructing a time integral window to accumulate the index to obtain a dynamic activity, and combining a static product total amount relative to an initial reference value to perform weighted summation, and if the comprehensive oxidation deterioration degree exceeds a threshold value, the deterioration is determined. That is, the scheme of the present application can effectively strip the environmental temperature interference, sensitively capture the early oxidation signal, and realize high-precision real-time early warning.

Description

Technical Field

[0001] This invention relates to the field of biofuel monitoring technology. More specifically, this invention relates to a method and system for assessing the oxidative degradation of biofuels based on conductivity characteristic analysis. Background Technology

[0002] Due to the relatively reactive chemical properties of biofuels such as fatty acid methyl esters, hydrogenated vegetable oils, and sustainable aviation fuels, especially biodiesel made from waste oils, they are highly susceptible to auto-oxidation during storage and transportation. This oxidation process typically includes an induction period, a rapid oxidation period, and a degradation period. The induction period is the most critical stage for fuel quality management. During this stage, although free radicals are generated within the fuel, changes in macroscopic physicochemical indicators such as acid value and viscosity are not significant. However, once the induction period is exceeded, the fuel quality will irreversibly and rapidly decline. Currently, the mainstream detection method mostly adopts the EN14112 standard (i.e., the Rancimat method). However, this method requires manual sampling and offline heating analysis, which is not only cumbersome and time-consuming but also exhibits significant lag. This non-real-time detection method cannot reflect the current state of the fuel in the storage tank in a timely manner, making it difficult to meet the needs of modern industry for real-time monitoring of storage and transportation safety.

[0003] In order to achieve real-time monitoring of oil condition and solve the lag problem of the above-mentioned offline detection methods, the industry has begun to try to use online conductivity sensors for evaluation, aiming to reflect the oxidation state of biofuels by detecting changes in their conductivity online.

[0004] However, biofuels have extremely low electrical conductivity, and this parameter is extremely sensitive to temperature. In practical applications, diurnal fluctuations in ambient temperature can cause significant physical drift in conductivity. This temperature drift caused by temperature changes is often more than an order of magnitude larger than the weak chemical signal generated by early oxidation. Due to the extremely low signal-to-noise ratio, existing online monitoring methods are prone to false alarms or missed alarms, making it difficult to accurately assess the oxidation state of oil products under complex environmental conditions. Summary of the Invention

[0005] The purpose of this invention is to propose a method and system for assessing the oxidative degradation of biofuels based on conductivity characteristic analysis, in order to solve the problem that online monitoring methods in the prior art are prone to false alarms or missed alarms; to this end, this invention provides solutions in the following two aspects.

[0006] In a first aspect, the present invention provides a method for assessing the oxidative degradation of biofuels based on conductivity characteristic analysis, comprising:

[0007] Real-time conductivity and temperature data of biofuel in the storage tank are collected and filtered to obtain the conductivity and temperature values ​​at the current sampling time. Based on a preset temperature compensation coefficient, the actual increment of the conductivity value at the current sampling time relative to the previous sampling time is calculated, and the theoretical conductivity increment caused only by temperature change is calculated. The absolute value of the difference between the actual increment and the theoretical conductivity increment is calculated to obtain the non-thermally induced rate of change residual. A sliding time window is constructed, and multiple non-thermally induced rate of change within the sliding time window are calculated. The standard deviation of the rate of change residual is calculated and logarithmically amplified by combining it with the conductivity value at the current sampling time to obtain the oxidation micro-instability index. A time integration window is constructed, and the oxidation micro-instability index within the time integration window is cumulatively summed to obtain the cumulative value of dynamic activity. The difference between the conductivity value at the current sampling time and the initial reference value is calculated to obtain the total amount of static products. The cumulative value of dynamic activity and the total amount of static products are weighted and summed to obtain the comprehensive oxidation deterioration degree. If the comprehensive oxidation deterioration degree exceeds a preset threshold, the biofuel is determined to have undergone oxidation deterioration.

[0008] Thus, by constructing a non-thermally induced rate of change residual and utilizing differential and compensation logic, the physical drift of electrical conductivity caused by environmental temperature fluctuations is mathematically eliminated. Even in outdoor storage tanks with large day-night temperature differences, extremely weak chemical change signals can be accurately identified.

[0009] By introducing an oxidation microinstability index, focusing on the "volatility" of the signal rather than simply the "amplitude," and utilizing the microinstability caused by the free radical reaction at the end of the induction period, we can provide significantly earlier warnings than traditional methods, gaining valuable time for response.

[0010] Preferably, the acquisition of real-time conductivity and temperature data of biofuel in the storage tank includes: synchronously acquiring raw data streams at a preset sampling frequency using conductivity and temperature sensors installed at the bottom and middle of the storage tank; performing moving average filtering on the raw data streams to eliminate high-frequency electromagnetic interference, and obtaining the conductivity and temperature values ​​at the current sampling time.

[0011] Preferably, the expression for the non-thermally induced rate of change residual is: In the formula, Indicates the current sampling time Non-thermally induced rate of change residuals and These represent the conductivity values ​​at the current sampling time and the previous sampling time, respectively. and These represent the temperature values ​​at the current sampling time and the previous sampling time, respectively. This represents the preset temperature compensation coefficient. This represents absolute value operations.

[0012] Preferably, the process of obtaining the oxidation microinstability index includes the step of calculating the residual mean: obtaining multiple non-thermal rate of change residuals within the sliding time window; calculating the arithmetic mean of all non-thermal rate of change residuals within the sliding time window to obtain the residual mean corresponding to the sliding time window.

[0013] Preferably, the expression for the oxidation microinstability index is: In the formula, Indicates the current sampling time The oxidation microinstability index, This indicates the length of the sliding time window. This represents the residual of the non-thermally induced rate of change within the sliding time window. This represents the mean residual value corresponding to the sliding time window. This represents the conductivity value at the current sampling time.

[0014] Preferably, the expression for the comprehensive oxidative deterioration degree is: In the formula, Indicates the current sampling time The overall degree of oxidation and deterioration, This indicates the length of the time integration window. This represents the oxidation microinstability index within the time integration window. This represents the conductivity value at the current sampling time. This represents the baseline conductivity value at the initial stage of biofuel entering the tank. Represents the dynamic weighting coefficient. This represents the thermodynamic weighting coefficient.

[0015] Preferably, if the overall oxidation degree exceeds a preset threshold, the biofuel is determined to have undergone oxidation and deterioration, including: when the overall oxidation degree exceeds a first preset threshold, the biofuel is determined to be at the end of the induction period, the sampling frequency is increased and manual sampling is prompted; when the overall oxidation degree exceeds a second preset threshold, the biofuel is determined to be in the rapid oxidation period, and protective measures are triggered, the protective measures including nitrogen sealing protection or circulating cooling; wherein, the second preset threshold is greater than the first preset threshold.

[0016] Preferably, the method for obtaining the temperature compensation coefficient is as follows: when the system is first run or when new biofuel is injected, a benchmark calibration is performed for a preset duration; during the stage when the biofuel has not deteriorated, data on the change in conductivity with temperature is recorded; and the temperature compensation coefficient is obtained by fitting and calculating the proportion of change in conductivity per unit temperature change.

[0017] Preferably, the biofuel includes fatty acid methyl esters, hydrogenated vegetable oils, or sustainable aviation fuel.

[0018] In the second aspect, a biofuel oxidative degradation assessment system based on conductivity characteristic analysis includes:

[0019] The processor; the memory storing computer instructions for assessing the oxidative degradation of biofuels based on conductivity characteristic analysis, which, when executed by the processor, cause the system to perform the aforementioned biofuel oxidative degradation assessment method based on conductivity characteristic analysis.

[0020] The beneficial effects of this invention are as follows: By constructing a dynamic correlation model between temperature and conductivity and calculating the residual of the non-thermally induced rate of change, the physical-thermal effect interference of environmental temperature fluctuations on the conductivity of biofuels is effectively eliminated at the mathematical level, solving the technical problem of strong thermal noise masking weak oxidation signals. By introducing the oxidation micro-instability index and the comprehensive oxidation degradation degree, this invention can sensitively capture the high-frequency micro-fluctuation characteristics caused by free radical reactions during the induction period, thereby identifying the end point of the oxidation induction period in advance before the deterioration of macroscopic physicochemical indicators such as acid value, achieving real-time online monitoring of biofuel storage safety with high sensitivity and low false alarm rate. Attached Figure Description

[0021] Figure 1 This schematically illustrates the steps of the biofuel oxidative degradation assessment method based on conductivity characteristic analysis in this embodiment.

[0022] Figure 2 A schematic diagram illustrating the dynamic changes of evaluation indicators according to embodiments of the present invention;

[0023] Figure 3 The diagram illustrates a comparison of the early warning triggering timeliness of the present invention and existing technologies. Detailed Implementation

[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0025] like Figure 1 As shown, the biofuel oxidation degradation assessment method based on conductivity characteristic analysis in this embodiment includes the following steps:

[0026] Step S1: Collect real-time conductivity data and real-time temperature data of biofuel in the storage tank, and filter the real-time conductivity data and real-time temperature data to obtain the conductivity value and temperature value at the current sampling time.

[0027] Specifically, in this embodiment of the invention, a high-sensitivity online conductivity sensor (range 0-2000 pS / m, accuracy ±0.1 pS / m) and a high-precision temperature sensor (accuracy ±0.1℃) are installed at the bottom and middle of the biofuel storage tank. The system synchronously acquires real-time conductivity data and real-time temperature data of the biofuel at a fixed sampling frequency; exemplarily, the sampling frequency is 1 Hz, i.e., once per second. To eliminate high-frequency electromagnetic interference from the sensor circuit itself, the raw data stream is subjected to moving average filtering. Let... At the current sampling time, after preprocessing, the conductivity and temperature values ​​at the current sampling time are obtained.

[0028] For example, if the original conductivity sequence collected by the sensor is [100.1, 100.3, 99.8, 100.2], after being filtered by a moving average of length 4, the current smoothed value is approximately 100.1 pS / m.

[0029] In this way, by filtering and processing the dataset, more accurate and smooth basic data can be obtained, providing a continuous and smooth basic data stream for subsequent calculations. This effectively eliminates high-frequency electromagnetic interference generated by the sensor circuit itself and the surrounding environment, ensuring the signal-to-noise ratio and accuracy of subsequent feature extraction.

[0030] Step S2: Based on the preset temperature compensation coefficient, calculate the actual increment of the conductivity value at the current sampling time relative to the previous sampling time, and calculate the theoretical conductivity increment caused only by temperature change. Calculate the absolute value of the difference between the actual increment and the theoretical conductivity increment to obtain the non-thermally induced rate of change residual.

[0031] Specifically, the electrical conductivity of biofuels essentially reflects the ability of ions to migrate, which increases with increasing temperature. In the absence of chemical alteration, changes in conductivity should be entirely explained by temperature changes. If the change in conductivity deviates from the theoretical trajectory derived from temperature changes, it indicates that an internal chemical reaction has occurred.

[0032] In this step, a "benchmark calibration" is first performed for a preset duration (e.g., 24 hours) when the system is first run or when new oil is injected. The temperature compensation coefficient of conductivity as a function of temperature is obtained during the period when the oil has not deteriorated.

[0033] Then, the residual value of the non-thermal induced rate of change is obtained according to the expression for calculating the residual in real time. The expression is as follows:

[0034] ;

[0035] In the formula, Indicates the current sampling time Non-thermally induced rate of change residuals and These represent the conductivity values ​​at the current sampling time and the previous sampling time, respectively. and These represent the temperature values ​​at the current sampling time and the previous sampling time, respectively. This represents the preset temperature compensation coefficient. This represents the absolute value operation. Among them, This represents the preset temperature compensation coefficient, in °C. -1 The value is usually between 0.01 and 0.05.

[0036] Among them, the non-thermal rate of change residual The construction of this system is based on a first-order Taylor expansion and difference approximation of the Arrhenius conductivity-temperature constitutive equation for electrolyte solutions. This approach allows the system to effectively eliminate the background drift caused by purely physical thermal motion without relying on complex fluid thermodynamic models.

[0037] Thus, by constructing the non-thermally induced rate of change residual and calculating the difference between the actual increment and the theoretical increment derived from the temperature compensation coefficient, the physical drift of conductivity caused by environmental temperature fluctuations can be effectively removed, and the instantaneous fluctuation amplitude of conductivity caused only by chemical composition disturbances can be extracted, thereby realizing the extraction of weak chemical signals in complex environments with large day-night temperature differences.

[0038] Step S3: Construct a sliding time window, calculate the standard deviation of multiple non-thermally induced rate of change residuals within the sliding time window, and logarithmically amplify the conductivity value at the current sampling time to obtain the oxidation microinstability index.

[0039] Oxidation reactions are random and non-uniform during the induction period. The generation and quenching of free radicals cause high-frequency fluctuations in the conductivity signal at the microscopic scale. The residuals alone may still contain sporadic noise, so statistical methods are needed to quantify the degree of this persistent fluctuation. Specifically, a sliding time window is set, for example, taking the past 300 sampling points.

[0040] In this step, the mean residual needs to be calculated first. The method to obtain it is as follows: obtain multiple non-thermal rate of change residuals within the sliding time window, calculate the arithmetic mean of all non-thermal rate of change residuals within the sliding time window, and obtain the mean residual corresponding to the sliding time window.

[0041] Then, using the calculated mean residual value, the oxidation microinstability index is calculated. The expression for the oxidation microinstability index is as follows:

[0042] ;

[0043] In the formula, Indicates the current sampling time The oxidation microinstability index, This indicates the length of the sliding time window. This represents the residual of the non-thermally induced rate of change within the sliding time window. This represents the mean residual value corresponding to the sliding time window. This represents the conductivity value at the current sampling time. , The 10 in the formula is a safety constant, used to prevent the logarithmic value from becoming too small and to ensure that the amplification factor is always greater than 1. It should be noted that the above logarithmic function... In The safety constant 10 has been normalized (dimensionless) by dividing by the reference unit of 1 pS / m before being substituted into the calculation, so as to meet the mathematical specifications of logarithmic operations in pure numerical form.

[0044] Among them, the oxidation microinstability index The construction integrates the local high-frequency fluctuation standard deviation with the global logarithmic gain. Its physical mechanism is that during the metamorphic induction period, the generation and quenching of free radicals will cause the microscopic ion mobility to exhibit transient pulses, thus producing high variance characteristics; while the introduction of the logarithmic amplification mechanism can better map the kinetic law of the exponential increase of products in the later stage of chemical bond breaking.

[0045] For example, assuming the residual standard deviation (square root part) calculated within the window is 0.5 pS / m, the current conductivity... pS / m, thus the oxidation microinstability index is 1.02. If the basic conductivity increases with increasing oxidation, it reaches... With a standard deviation of fluctuation of 0.5, the calculated value of the oxidation microinstability index is 1.5, indicating that the same fluctuation is given a greater weight risk in the later stages.

[0046] Thus, by using statistical standard deviation and logarithmic amplification, the microscopic high-frequency fluctuations caused by free radical reactions during the induction period can be sensitively captured, and the sensitivity to mid-to-late stage deterioration can be automatically increased as the base value increases.

[0047] Step S4: Construct a time integration window, accumulate and sum the oxidation microinstability index within the time integration window to obtain the dynamic activity accumulation value, calculate the difference between the conductivity value at the current sampling time and the initial reference value to obtain the total static product, and perform a weighted summation of the dynamic activity accumulation value and the total static product to obtain the comprehensive oxidation deterioration degree. If the comprehensive oxidation deterioration degree exceeds a preset threshold, it is determined that the biofuel has undergone oxidation deterioration.

[0048] Specifically, fluctuations at a single point may be caused by external disturbances, while true oxidative deterioration is a cumulative process. This step defines a relatively long time integration window, such as the past 3600 sampling points.

[0049] The expression for the overall degree of oxidative deterioration is:

[0050] ;

[0051] In the formula, Indicates the current sampling time The overall degree of oxidation deterioration (dimensionless score). This indicates the length of the time integration window. This represents the oxidation microinstability index within the time integration window. This represents the conductivity value at the current sampling time. This represents the baseline conductivity value at the initial stage of biofuel entering the tank. Represents the dynamic weighting coefficient. This represents the thermodynamic weighting coefficient; to ensure the final It is a dimensionless pure fraction. and The units are set to the reciprocals of the dimensions of the corresponding integral and incremental terms, respectively. In this embodiment, the kinetic weighting coefficient is set to 0.01, and the thermodynamic weighting coefficient is set to 1.0.

[0052] Among them, the overall oxidation degree A dynamic-static dual-channel evaluation mechanism was constructed, which integrates and weights the dynamic activity (integral term) characterizing the intensity of the chain reaction and the static displacement (difference term) characterizing the total amount of oxidative polar products, thereby constructing a state observation model with good noise resistance and robustness.

[0053] For example, assuming the cumulative sum of the oxidation microinstability index over the past hour is 5000, the current conductivity... initial conductivity Therefore, the overall oxidation degree can be calculated to be 70.

[0054] If the first preset threshold (yellow warning) is 60 and the second preset threshold (red warning) is 100, then under the current circumstances, the value of the overall oxidation deterioration exceeds the yellow threshold but does not exceed the red threshold.

[0055] The logic for determining whether biofuels have undergone oxidative deterioration is as follows:

[0056] when When the first preset threshold is exceeded, the induction period is considered over, the system automatically increases the sampling frequency and prompts for manual sampling; when When the second preset threshold is exceeded, it is determined to be a rapid oxidation period, and the system will automatically trigger nitrogen sealing protection or circulating cooling measures.

[0057] Thus, by integrating the cumulative dynamic activity and the total static product, a robust comprehensive evaluation index is constructed. This index not only amplifies early trends through the cumulative effect but also ensures the reliability of the judgment results through benchmark comparison. This allows the invention to capture both trends that are "about to change but have not yet changed" and facts that have "already deteriorated," effectively avoiding false alarms caused by fluctuations in single-point data and improving the accuracy of alarms.

[0058] The following is combined with Figure 2 and Figure 3 The technical effects achieved by this invention will be further explained.

[0059] Figure 2 The graph shows the dynamic changes of the evaluation indicators. In the graph, the oxidation micro-instability index is almost silent in the early stage, but high-frequency pulses appear in the later stage. The overall oxidation degradation exhibits a typical "hockey stick" effect: during the stable period, it closely approaches 0 (no false alarm accumulation), indicating that the algorithm has extremely high anti-interference ability during the safe period and will not cause false alarms due to temperature changes; once it enters the active oxidation period, the curve rises sharply in a non-linear manner and quickly crosses the set yellow warning threshold line. This is because the algorithm uses a cumulative effect, amplifying the weak early signals. That is, Figure 3 The algorithm demonstrates its excellent engineering characteristics: "It remains silent under normal circumstances and quickly alerts when anomalies occur."

[0060] Figure 3 A comparison chart showing the timeliness of early warning triggering between the present invention and existing technologies is presented. With an alarm threshold of 40, the chart reveals that existing technologies, relying on absolute value changes, exhibit a significant delay in response, triggering an alarm only after approximately 98 hours. By this time, the oil may have already undergone severe oxidation, missing the optimal treatment window. In contrast, the present invention responds sensitively immediately after the 72-hour induction period, triggering an alarm around 84 hours. At this point, the oil is just entering the early stages of rapid oxidation, leaving room for recovery. In other words, the present invention issues an early warning approximately 14 hours earlier than existing technologies, providing companies with valuable time for timely intervention and effectively preventing oil spoilage.

[0061] This invention also provides a biofuel oxidative degradation assessment system based on conductivity characteristic analysis. The system includes a processor and a memory, the memory storing computer program instructions. When the processor executes the computer program instructions, it implements the biofuel oxidative degradation assessment method based on conductivity characteristic analysis described above according to this invention.

[0062] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and therefore will not be described in detail here.

[0063] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented by computer-readable / executable instructions stored or otherwise maintained on such a computer-readable medium.

[0064] In the description of this specification, "multiple" means at least two, such as two, three or more, etc., unless otherwise expressly and specifically defined.

[0065] While various embodiments of the invention have been shown and described in this specification, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention.

Claims

1. A method for assessing the oxidative degradation of biofuels based on conductivity characteristic analysis, characterized in that, include: Real-time conductivity and temperature data of biofuel in the storage tank are collected, and the real-time conductivity and temperature data are filtered to obtain the conductivity and temperature values ​​at the current sampling time. Based on a preset temperature compensation coefficient, the actual increment of the conductivity value at the current sampling moment relative to the previous sampling moment is calculated, and the theoretical conductivity increment caused solely by temperature change is also calculated. The absolute value of the difference between the actual increment and the theoretical conductivity increment is then calculated to obtain the non-thermally induced rate of change residual, satisfying the following: ； Indicates the current sampling time Non-thermally induced rate of change residuals and These represent the conductivity values ​​at the current sampling time and the previous sampling time, respectively. and These represent the temperature values ​​at the current sampling time and the previous sampling time, respectively. This represents the preset temperature compensation coefficient. This represents the absolute value operation; A sliding time window is constructed, and the standard deviation of multiple non-thermally induced rate-of-change residuals within the sliding time window is calculated. This standard deviation is then logarithmically amplified by combining the conductivity value at the current sampling moment to obtain the oxidation microinstability index, which satisfies the following: ； Indicates the current sampling time The oxidation microinstability index, This indicates the length of the sliding time window. This represents the residual of the non-thermally induced rate of change within the sliding time window. This represents the mean residual value corresponding to the sliding time window. This represents the conductivity value at the current sampling time. A time integration window is constructed, and the cumulative value of dynamic activity is obtained by summing the oxidation microinstability index within the time integration window. The difference between the conductivity value at the current sampling time and the initial reference value is calculated to obtain the total static product. The cumulative value of dynamic activity and the total static product are weighted and summed to obtain the comprehensive oxidation degradation degree, which satisfies: ； Indicates the current sampling time The overall degree of oxidation and deterioration, This indicates the length of the time integration window. This represents the oxidation microinstability index within the time integration window. This represents the baseline conductivity value at the initial stage of biofuel entering the tank. Represents the dynamic weighting coefficient. Indicates the thermodynamic weighting coefficient; If the overall degree of oxidation deterioration exceeds a preset threshold, the biofuel is determined to have undergone oxidation deterioration.

2. The biofuel oxidative degradation assessment method based on conductivity characteristic analysis according to claim 1, characterized in that, The collection of real-time conductivity and temperature data of biofuel in the storage tank includes: The raw data stream is synchronously acquired at a preset sampling frequency by using conductivity and temperature sensors installed at the bottom and middle of the storage tank. The original data stream is subjected to moving average filtering to eliminate high-frequency electromagnetic interference, thereby obtaining the conductivity and temperature values ​​at the current sampling time.

3. The biofuel oxidative degradation assessment method based on conductivity characteristic analysis according to claim 1, characterized in that, The process of obtaining the oxidation microinstability index includes the step of calculating the mean residual: Obtain multiple non-thermally induced rate of change residuals within the sliding time window; Calculate the arithmetic mean of all non-thermally induced rate of change residuals within the sliding time window to obtain the mean residual value corresponding to the sliding time window.

4. The biofuel oxidative degradation assessment method based on conductivity characteristic analysis according to claim 1, characterized in that, If the overall degree of oxidation deterioration exceeds a preset threshold, the biofuel is determined to have undergone oxidation deterioration, including: When the overall oxidation degree exceeds the first preset threshold, the biofuel is determined to be in the end stage of the induction period, the sampling frequency is increased and manual sampling is prompted. When the overall oxidation degree exceeds the second preset threshold, the biofuel is determined to be in a rapid oxidation period, and protective measures are triggered, including nitrogen sealing protection or circulating cooling. Wherein, the second preset threshold is greater than the first preset threshold.

5. The biofuel oxidative degradation assessment method based on conductivity characteristic analysis according to claim 1, characterized in that, The method for obtaining the temperature compensation coefficient is as follows: When the system is first run or when new biofuel is injected, a baseline calibration for a preset duration is performed. Data on the change in electrical conductivity with temperature were recorded during the pre-deterioration stage of the biofuel. The temperature compensation coefficient is obtained by fitting and calculating the proportion of change in conductivity per unit temperature change.

6. The biofuel oxidative degradation assessment method based on conductivity characteristic analysis according to claim 1, characterized in that, The biofuels include fatty acid methyl esters, hydrogenated vegetable oils, or sustainable aviation fuels.

7. A biofuel oxidative degradation assessment system based on conductivity characteristic analysis, characterized in that, include: processor; A memory storing computer instructions for assessing the oxidative degradation of biofuels based on conductivity characteristic analysis, wherein when the computer instructions are executed by the processor, the system performs the biofuel oxidative degradation assessment method based on conductivity characteristic analysis according to any one of claims 1-6.

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