Health evaluation and life prediction method and system based on liquid characteristic parameters

Abstract

The application discloses a health evaluation and life prediction method and system based on liquid characteristic parameters, wherein the method comprises the following steps: acquiring original parameter data output by a liquid characteristic sensor, wherein the original parameter data at least comprises temperature, dynamic viscosity, density, dielectric constant and resistance; screening the original parameter data according to a temperature threshold to obtain screening data; performing temperature regression calculation according to the screening data to obtain regression dielectric constant, regression conductivity and regression kinematic viscosity; calculating water content, acid value and pollution grade according to the regression dielectric constant and the regression conductivity; performing weighted calculation according to the regression kinematic viscosity, the acid value, the pollution grade, the water content, the regression dielectric constant, the regression conductivity, temperature and working time to determine a liquid health degree score; and determining a liquid residual service life according to the liquid health degree score and cumulative working time. The overall calculation process from original parameter acquisition to health evaluation and life determination is formed.

Description

Technical Field

[0001] This application relates to the field of equipment operation status monitoring and data processing technology, specifically to a health assessment and life prediction method based on liquid characteristic parameters, a health assessment and life prediction system based on liquid characteristic parameters, an electronic device, and a readable storage medium. Background Technology

[0002] Hydraulic systems, engines, and transformers typically rely on hydraulic fluid for lubrication, transmission, and insulation during operation. In existing technologies, oil change intervals for most equipment are determined primarily based on manufacturer-recommended fixed operating times or manual sampling results. While some systems are equipped with fluid characteristic sensors to acquire data such as temperature, viscosity, and electrical parameters, this data is usually displayed as individual indicators or simple threshold alarms, lacking a unified data processing and comprehensive evaluation system. Because hydraulic fluids are affected by temperature, load, and contamination under actual operating conditions, their physicochemical parameters exhibit dynamic changes, making it difficult for a single parameter or static threshold to fully reflect the current state of the fluid.

[0003] Existing solutions often focus on monitoring single indicators or rely on experience-based judgment, making it difficult to transform multi-source parameters into quantifiable overall status assessment results, thus failing to provide a unified evaluation basis for oil change decisions. Summary of the Invention

[0004] The purpose of this application is to provide a health assessment and life prediction method based on liquid characteristic parameters, a health assessment and life prediction system based on liquid characteristic parameters, an electronic device, and a readable storage medium, which can solve the problem in the prior art that it is impossible to perform systematic calculations based on multiple original liquid parameters and form a comprehensive health assessment and remaining service life determination.

[0005] To solve the above-mentioned technical problems, this application is implemented as follows: In a first aspect, embodiments of this application provide a method for health assessment and lifespan prediction based on liquid characteristic parameters, the method comprising: Acquire raw parameter data output by the liquid property sensor, wherein the raw parameter data includes at least temperature, dynamic viscosity, density, dielectric constant, and resistance. The original parameter data is filtered based on a temperature threshold to obtain filtered data; Temperature regression calculations were performed based on the screened data to obtain the regression dielectric constant, regression conductivity, and regression kinematic viscosity. Calculate the water content, acid value, and pollution level based on the regression dielectric constant and the regression conductivity; The liquid health score is determined by weighting the regression kinematic viscosity, acid value, pollution level, water content, regression dielectric constant, regression conductivity, temperature, and working time. The remaining service life of the liquid is determined based on the liquid health score and the cumulative working time.

[0006] Optionally, the step of performing temperature regression calculation based on the screened data to obtain the regression dielectric constant includes: An exponential function model is constructed based on the selected dielectric constant and the corresponding temperature. The dielectric constant is then corrected for temperature to obtain the regression dielectric constant at the reference temperature.

[0007] Optionally, the step of performing temperature regression calculation based on the screened data to obtain the regression conductivity includes: Based on the selected resistance values, establish a model showing the reciprocal relationship between resistance and conductivity. The regressed conductivity at the reference temperature is obtained by performing a nonlinear transformation based on the reciprocal relationship model.

[0008] Optionally, the step of performing temperature regression calculation based on the filtered data to obtain the regression kinematic viscosity includes: The regression dynamic viscosity at the reference temperature is obtained by performing nonlinear regression calculations based on the selected dynamic viscosity and the corresponding temperature. The regression kinematic viscosity is calculated based on the regression dynamic viscosity and the density.

[0009] Optionally, the step of calculating water content, acid value, and pollution level based on the regression dielectric constant and the regression conductivity includes: Water content is determined by constructing an exponential model based on the regression dielectric constant. The acid value is determined by constructing a logarithmic model based on the regression conductivity and the regression dielectric constant. The pollution level is determined by constructing a linear combination model based on the regression conductivity and the regression dielectric constant.

[0010] Optionally, the step of determining the liquid health score by weighting the regression kinematic viscosity, acid value, contamination level, water content, regression dielectric constant, regression conductivity, temperature, and operating time includes: The average values ​​of the regressive kinematic viscosity, acid value, pollution level, water content, regressive conductivity, and temperature are calculated for multiple consecutive periods according to a preset time interval. For the regressive kinematic viscosity, the interval is determined based on the average value of multiple consecutive periods and the preset viscosity interval, and the trend is determined based on the rate of change between adjacent periods, so as to determine the individual score corresponding to the regressive kinematic viscosity. For the acid value, the interval is determined by comparing the average value of multiple consecutive periods with the preset acid value interval, and the trend is determined by the rate of change between adjacent periods, so as to determine the individual score corresponding to the acid value. For the pollution level, the level is determined by comparing the average value of multiple consecutive periods with a preset pollution level range, and the individual score corresponding to the pollution level is determined. For the moisture content, the interval is determined by comparing the average value of multiple consecutive periods with the preset moisture content interval, and the trend is determined by the rate of change between adjacent periods, so as to determine the individual score corresponding to the moisture content. For the regressed conductivity, the interval is determined by comparing the average value of multiple consecutive periods with the preset conductivity interval, and the trend is determined by the rate of change between adjacent periods, so as to determine the individual score corresponding to the regressed conductivity. For the temperature, the interval is determined by comparing the average value of multiple consecutive periods with the preset temperature range, and the trend is determined by the rate of change between adjacent periods, thus determining the individual score corresponding to the temperature. For the aforementioned working time, a time scoring model is constructed based on the cumulative working time and the preset recommended working time to determine the individual score corresponding to the working time; The scores of each item are weighted and summed according to preset weights to obtain the liquid health score.

[0011] Optionally, determining the remaining service life of the liquid based on the liquid health score and cumulative working time includes: The lifespan correction factor is determined based on the interval in which the liquid health score falls. A segmented calculation model is constructed based on the recommended working time, the cumulative working time, and the lifespan correction coefficient to determine the remaining lifespan of the liquid. When the liquid health score is lower than a preset threshold, the remaining service life of the liquid is determined to be zero.

[0012] Secondly, embodiments of this application provide a health assessment and lifespan prediction system based on liquid characteristic parameters, the system comprising: The raw parameter acquisition module is used to acquire raw parameter data output by the liquid property sensor. The raw parameter data includes at least temperature, dynamic viscosity, density, dielectric constant, and resistance. The raw parameter filtering module is used to filter the raw parameter data according to a temperature threshold to obtain filtered data; The temperature regression calculation module is used to perform temperature regression calculations based on the filtered data to obtain the regression dielectric constant, regression conductivity, and regression kinematic viscosity. The dielectric conductivity calculation module is used to calculate the water content, acid value, and pollution level based on the regression dielectric constant and the regression conductivity. The health score calculation module is used to perform weighted calculations based on the regression kinematic viscosity, the acid value, the pollution level, the water content, the regression dielectric constant, the regression conductivity, the temperature, and the working time to determine the liquid health score; The lifespan calculation module is used to determine the remaining lifespan of the liquid based on the liquid health score and the cumulative working time.

[0013] Optionally, the temperature regression calculation module includes: The regression dielectric constant determination module is used to construct an exponential function model based on the selected dielectric constant and the corresponding temperature, and to perform temperature correction on the dielectric constant to obtain the regression dielectric constant at the reference temperature.

[0014] Optionally, the temperature regression calculation module includes: The reciprocal relationship model building module is used to establish a reciprocal relationship model between resistance and conductivity based on the selected resistance values. The regression conductivity determination module is used to perform a nonlinear transformation based on the reciprocal relationship model to obtain the regression conductivity at the reference temperature.

[0015] Optionally, the temperature regression calculation module includes: The regression dynamic viscosity determination module is used to perform nonlinear regression calculations based on the screened dynamic viscosity and the corresponding temperature to obtain the regression dynamic viscosity at the reference temperature. The regression kinematic viscosity determination module is used to calculate the regression kinematic viscosity based on the regression dynamic viscosity and the density.

[0016] Optionally, the dielectric conductivity calculation module includes: A moisture content determination module is used to construct an exponential model based on the regression dielectric constant to determine the moisture content. The acid value determination module is used to determine the acid value by constructing a logarithmic model based on the regression conductivity and the regression dielectric constant. The pollution level determination module is used to construct a linear combination model based on the regression conductivity and the regression dielectric constant to determine the pollution level.

[0017] Optionally, the health score calculation module includes: The average value calculation module is used to calculate the average value of the regression kinematic viscosity, the acid value, the pollution level, the water content, the regression conductivity, and the temperature for multiple consecutive periods according to a preset time interval. The single-item score determination module is used to determine the single-item score corresponding to the regression kinematic viscosity by performing interval determination based on the average value of multiple consecutive periods and a preset viscosity range, and by performing trend determination based on the rate of change between adjacent periods; for the acid value, it performs interval determination based on the average value of multiple consecutive periods and a preset acid value range, and by performing trend determination based on the rate of change between adjacent periods; for the pollution level, it performs level determination based on the average value of multiple consecutive periods and a preset pollution level range, and determines the single-item score corresponding to the pollution level; for the water content, it performs determination based on the average value of multiple consecutive periods and a preset water content range. The following methods are used to determine the individual score for the water content: For the regressive conductivity, the method involves determining the individual score based on the average value of multiple consecutive periods and a preset conductivity range, and then determining the individual score based on the rate of change between adjacent periods; for the temperature, the method involves determining the individual score based on the average value of multiple consecutive periods and a preset temperature range, and then determining the individual score based on the rate of change between adjacent periods; and for the working time, a time scoring model is constructed based on the cumulative working time and a preset recommended working time to determine the individual score for the working time. The liquid health score determination module is used to perform a weighted summation of the individual scores according to preset weights to obtain the liquid health score.

[0018] Optionally, the lifespan calculation module includes: The lifespan correction factor determination module is used to determine the lifespan correction factor based on the interval in which the liquid health score falls. The liquid remaining service life determination module is used to construct a segmented calculation model based on the recommended working time, the cumulative working time and the service life correction coefficient to determine the liquid remaining service life. The liquid remaining lifespan determination module is also used to determine the liquid remaining lifespan as zero when the liquid health score is lower than a preset threshold.

[0019] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0020] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0021] In this embodiment, multiple raw parameter data output by the liquid property sensor are acquired, and data filtering, temperature regression calculation, and correlation parameter estimation are performed on this basis. Furthermore, various parameters are combined to perform weighted calculation to form a liquid health score, and the remaining service life is determined accordingly. This transforms the originally scattered single detection data into a comprehensive index with a unified evaluation caliber. At the same time, real-time status assessment and service life determination are linked, thereby constructing a complete calculation chain from the acquisition of raw parameters to the output of service life results, so that the oil change decision has a continuous and consistent quantitative basis. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the steps of a health assessment and lifespan prediction method based on liquid property parameters according to an embodiment of this application. Figure 2 This is a schematic diagram of the structure of a health assessment and life prediction system based on liquid property parameters according to an embodiment of this application; Figure 3 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0025] This application provides a health assessment and lifespan prediction scheme based on liquid characteristic parameters. First, raw parameter data, including temperature, dynamic viscosity, density, dielectric constant, and resistance, are acquired and filtered according to a temperature threshold. Based on the filtered data, temperature regression calculations are performed to obtain the regression dielectric constant, regression conductivity, and regression kinematic viscosity. Then, water content, acid value, and contamination level are calculated based on the regression dielectric constant and regression conductivity. Subsequently, a weighted calculation is performed combining the regression kinematic viscosity, acid value, contamination level, water content, regression dielectric constant, regression conductivity, temperature, and operating time to determine the liquid health score. Finally, the remaining service life of the liquid is determined based on the liquid health score and cumulative operating time, forming an overall calculation process from acquiring raw parameters to health assessment and lifespan determination.

[0026] The following description, in conjunction with the accompanying drawings, details a health assessment and life prediction scheme based on liquid characteristic parameters provided in this application, through specific embodiments and application scenarios.

[0027] Reference Figure 1 The diagram illustrates a flowchart of a health assessment and life prediction method based on liquid property parameters according to an embodiment of this application.

[0028] Step 101: Obtain the raw parameter data output by the liquid property sensor. The raw parameter data includes at least temperature, dynamic viscosity, density, dielectric constant, and resistance.

[0029] Liquid property sensors are used to continuously acquire the physical and electrical properties of the monitored liquid, and their output data forms the basis for subsequent calculations. The raw parameter data includes at least temperature, dynamic viscosity, density, dielectric constant, and resistance. Temperature reflects the current thermal state of the liquid, dynamic viscosity characterizes the intermolecular friction characteristics within the liquid, density characterizes the mass per unit volume, dielectric constant reflects the liquid's polarization ability in an electric field, and resistance reflects the liquid's ability to block electric current. These parameters are physically related; for example, temperature changes affect dynamic viscosity, dielectric constant, and resistance. Therefore, it is necessary to maintain the time synchronization of these parameters during the data acquisition phase to ensure consistency in subsequent calculations.

[0030] Step 102: Filter the original parameter data according to the temperature threshold to obtain filtered data.

[0031] Since parameters such as dynamic viscosity, dielectric constant, and resistance are all related to temperature, direct comparison of data from different temperature ranges within the same evaluation system may lead to biases. Therefore, before proceeding with temperature regression calculations, the original parameter data needs to be screened under unified conditions. A temperature threshold is used as a screening criterion to limit the range of data participating in subsequent calculations. The screening process is based on the temperature field in the original parameter data, retaining the original parameter data that meets the temperature threshold condition as the screened data, and excluding data that does not meet the condition from the subsequent calculation range. The screened data still structurally includes all fields such as temperature, dynamic viscosity, density, dielectric constant, and resistance; only the range of the data set changes.

[0032] Step 103: Perform temperature regression calculation based on the screened data to obtain the regression dielectric constant, regression conductivity, and regression kinematic viscosity.

[0033] The purpose of temperature regression calculation is to transform parameter values ​​collected under different temperature conditions to equivalent parameter values ​​at a unified reference temperature, so as to facilitate horizontal comparison and subsequent evaluation calculations. First, a temperature correction relationship is established based on the dielectric constant and corresponding temperature in the screened data to obtain the regression dielectric constant. Second, a functional relationship between resistance and conductivity is established based on the resistance values ​​in the screened data, and the regression conductivity is obtained through transformation. Third, the relationship between dynamic viscosity and temperature in the screened data is corrected to obtain the regression dynamic viscosity, and the regression kinematic viscosity is calculated in conjunction with density.

[0034] Step 104: Calculate the water content, acid value, and pollution level based on the regression dielectric constant and regression conductivity.

[0035] Moisture content, acid value, and pollution level are all evaluation indicators derived from electrical parameters. There is a correlation between moisture content and the regression dielectric constant; a functional relationship exists between acid value and both regression conductivity and regression dielectric constant; and the pollution level can also be calculated comprehensively using regression conductivity and regression dielectric constant. This step relies on regression parameters in its calculation logic and no longer uses the original dielectric constant or original resistance value.

[0036] Step 105: The liquid health score is determined by weighted calculation based on the regression kinematic viscosity, acid value, contamination level, water content, regression dielectric constant, regression conductivity, temperature, and working time.

[0037] First, regression kinematic viscosity, acid value, contamination level, water content, regression conductivity, and temperature are statistically analyzed according to preset time intervals to form a periodic data set. Second, individual scores are determined based on the correspondence between each parameter and preset reasonable intervals, while also considering the changes in parameters between adjacent periods. For working time, the corresponding score is determined based on the relationship between cumulative working time and preset recommended working time. All individual scores are weighted and summed according to preset weights to obtain a liquid health score. This liquid health score is a comprehensive indicator used to reflect the current state of the liquid.

[0038] Step 106: Determine the remaining service life of the liquid based on the liquid health score and cumulative working time.

[0039] First, the corresponding lifespan correction coefficient is determined based on the liquid health score's range. Second, the recommended working time is compared with the cumulative working time, and a calculation model is constructed using the lifespan correction coefficient to obtain the liquid's remaining lifespan. When the liquid health score is in a low range, the remaining lifespan is processed according to the corresponding rules. The remaining lifespan is a time-quantified result, used to characterize the duration of continued use in the current state.

[0040] In this embodiment, multiple raw parameter data output by the liquid property sensor are acquired, and data filtering, temperature regression calculation, and correlation parameter estimation are performed on this basis. Furthermore, various parameters are combined to perform weighted calculation to form a liquid health score, and the remaining service life is determined accordingly. This transforms the originally scattered single detection data into a comprehensive index with a unified evaluation caliber. At the same time, real-time status assessment and service life determination are linked, thereby constructing a complete calculation chain from the acquisition of raw parameters to the output of service life results, so that the oil change decision has a continuous and consistent quantitative basis.

[0041] In one exemplary embodiment of this application, one way to obtain the regressed dielectric constant by performing temperature regression calculation based on the screened data is as follows: an exponential function model is constructed based on the screened dielectric constant and the corresponding temperature, and the dielectric constant is corrected for temperature to obtain the regressed dielectric constant at the reference temperature.

[0042] In this implementation, the dielectric constant in the selected data corresponds to the temperature holding time. Considering that the dielectric constant exhibits a non-linear trend with temperature change—generally decreasing as temperature increases and relatively increasing as temperature decreases—an exponential function is used for correction when constructing the temperature regression model. Specifically, the selected dielectric constant is used as the current value, and the difference between the corresponding temperature and the reference temperature is used as the independent variable. A correction coefficient is established using an exponential function, allowing the dielectric constant to be converted to an equivalent value under the reference temperature condition under different temperature conditions. The coefficients in the exponential function model are determined based on historical test data and maintained within a unified system setting.

[0043] In the actual calculation process, each piece of screened data corresponds to a regression dielectric constant value. This regression dielectric constant is no longer directly affected by the original temperature fluctuations, thus providing a unified benchmark for subsequent calculations of water content, acid value, and pollution level. This implementation method does not reduce or compensate the screened data; it only performs temperature correction at the numerical level. The regression dielectric constant generated after calculation is stored as an independent field for use in subsequent steps for calculating derived parameters and health scores.

[0044] By constructing an exponential function model to correct the dielectric constant for temperature, the dielectric constants collected under different temperature conditions have a unified basis for comparison, avoiding direct interference from temperature fluctuations in subsequent calculations, and providing stable input conditions for the calculation of water content, acid value and pollution level.

[0045] In one exemplary embodiment of this application, one method of obtaining the regressed conductivity by performing temperature regression calculation based on the screened data is as follows: establishing a reciprocal relationship model between resistance and conductivity based on the screened resistance values; performing a nonlinear transformation based on the reciprocal relationship model to obtain the regressed conductivity at the reference temperature.

[0046] In this implementation, the resistance value in the filtered data is used as the electrical parameter input. There is an inverse relationship between conductivity and resistivity, and resistance reflects the resistive properties of a liquid. Therefore, parameter conversion can be achieved by constructing a functional model between resistance and conductivity. First, a basic model is established based on the physical relationship that resistivity and conductivity are reciprocals. Then, combining the dimensions of the resistance value in the system and the sensor output format, an inverse relationship model suitable for data calculation is constructed.

[0047] Considering that actual data changes are not strictly linear, a nonlinear function is further used to correct the reciprocal transformation, in order to better reflect the changing trends of resistance values ​​across different ranges. This nonlinear transformation process is directly based on the selected resistance values, without introducing a separate temperature variable, thus maintaining consistency with the logic of the regression dielectric constant calculation.

[0048] After conversion, the regressed conductivity is obtained, which numerically corresponds to the equivalent conductivity under the reference temperature conditions. The regressed conductivity, together with the regressed dielectric constant, forms the basis of the electrical evaluation and is used for subsequent calculations of water content, acid value, and pollution level.

[0049] By establishing a model of the inverse relationship between resistance and conductivity and performing a nonlinear transformation, resistance data can be converted into regressive conductivity under a unified benchmark, providing a consistent data source for subsequent derivative calculations based on electrical parameters.

[0050] In one exemplary embodiment of this application, one implementation of performing temperature regression calculation based on the screened data to obtain the regressed kinematic viscosity is as follows: performing nonlinear regression calculation based on the screened dynamic viscosity and the corresponding temperature to obtain the regressed dynamic viscosity at the reference temperature; and calculating the regressed kinematic viscosity based on the regressed dynamic viscosity and density.

[0051] In this embodiment, there is a negative correlation between dynamic viscosity and temperature; dynamic viscosity decreases as temperature increases. Therefore, a nonlinear function is used to correct the dynamic viscosity for temperature. Using the selected dynamic viscosity and its corresponding temperature as input, the regression dynamic viscosity under the reference temperature condition is calculated through a preset function model.

[0052] After obtaining the regressive dynamic viscosity, the regressive kinematic viscosity is calculated by combining it with the density from the screened data, based on the relationship that kinematic viscosity equals dynamic viscosity divided by density. This process maintains the fundamental relationship between physical quantities and does not introduce additional variables.

[0053] Regression kinematic viscosity is an important parameter for evaluating lubrication performance and has a high weight in the calculation of health score. Therefore, this implementation method ensures that its value is consistent with the reference temperature conditions.

[0054] By performing nonlinear regression on dynamic viscosity and combining it with density to calculate the regression kinematic viscosity, data under different temperature conditions are converted into a unified benchmark, providing a stable viscosity index for subsequent health assessment.

[0055] In one exemplary embodiment of this application, one method for calculating water content, acid value, and pollution level based on regression dielectric constant and regression conductivity is as follows: determining water content by constructing an exponential model based on regression dielectric constant; determining acid value by constructing a logarithmic model based on regression conductivity and regression dielectric constant; and determining pollution level by constructing a linear combination model based on regression conductivity and regression dielectric constant.

[0056] This implementation method calculates derived indices based on the functional relationship between electrical parameters. First, an exponential model is constructed using the regression dielectric constant to determine the water content. Since changes in water content significantly affect the dielectric constant, the relationship between the two can be described using an exponential function. This exponential model uses the regression dielectric constant as the input variable and obtains the water content value through function calculations.

[0057] Secondly, a logarithmic model is constructed based on the regression conductivity and regression dielectric constant to determine the acid value. This model obtains the acid value by exponentially calculating the regression conductivity and then performing a logarithmic calculation on the regression dielectric constant. The coefficients in the model are preset based on the system parameters and remain consistent.

[0058] Next, a linear combination model is constructed based on the regression conductivity and regression dielectric constant to determine the pollution level. The pollution level is obtained by proportionally calculating the regression dielectric constant and linearly combining it with the regression conductivity. This calculation logic maintains the correlation between electrical parameters without introducing new variables.

[0059] The above three indicators are all calculated based on regression electrical parameters. After the calculation is completed, a sequence of water content, acid value and pollution level is formed, which serves as input data for subsequent liquid health scoring.

[0060] By constructing exponential, logarithmic, and linear combination models, the regression dielectric constant and regression conductivity are converted into water content, acid value, and pollution level, realizing a systematic mapping of electrical parameters to state indicators and providing a data foundation for comprehensive evaluation.

[0061] In one exemplary embodiment of this application, a weighted calculation based on regressive kinematic viscosity, acid value, contamination level, water content, regressive dielectric constant, regressive conductivity, temperature, and working time is used to determine the liquid health score. This is achieved by: calculating the average values ​​of regressive kinematic viscosity, acid value, contamination level, water content, regressive conductivity, and temperature over multiple consecutive periods according to a preset time interval; for regressive kinematic viscosity, a range determination is made based on the average values ​​of multiple consecutive periods and a preset viscosity range, and a trend determination is made based on the rate of change between adjacent periods to determine the individual score corresponding to the regressive kinematic viscosity; for acid value, a range determination is made based on the average values ​​of multiple consecutive periods and a preset acid value range, and a trend determination is made based on the rate of change between adjacent periods to determine the individual score corresponding to the acid value; for contamination level, a range determination is made based on the average values ​​of multiple consecutive periods and a preset contamination level range. The system performs several assessments to determine the pollution level and its corresponding individual score. For water content, it uses the average value of multiple consecutive periods and a preset water content range to determine the range, and then uses the rate of change between adjacent periods to determine the trend, thus determining the individual score for water content. Similarly, for regressive conductivity, it uses the average value of multiple consecutive periods and a preset conductivity range to determine the range, and then uses the rate of change between adjacent periods to determine the trend, thus determining the individual score for regressive conductivity. For temperature, it uses the average value of multiple consecutive periods and a preset temperature range to determine the range, and then uses the rate of change between adjacent periods to determine the trend, thus determining the individual score for temperature. For working time, it constructs a time-based scoring model based on cumulative working time and a preset recommended working time to determine the individual score for working time. Finally, it weights and sums the individual scores according to preset weights to obtain the liquid health score.

[0062] First, the regression kinematic viscosity, acid value, pollution level, water content, regression conductivity, and temperature were statistically analyzed according to a preset time interval. The preset time interval was divided into multiple consecutive periods, and the average value of the corresponding parameter was calculated for each period. These consecutive periods were arranged chronologically to reflect the changes in the parameters at different stages. During the statistical process, only data meeting the pre-selection criteria were calculated to ensure consistency among the parameters.

[0063] For regressing kinematic viscosity, the first step is to determine the range based on the average value of multiple consecutive periods and a preset viscosity range. If the average value of each period falls within the preset viscosity range, a base score is formed; if any period exceeds the range, the score is deducted according to the deviation. Then, the rate of change between adjacent periods is calculated, and a trend score is formed based on whether the rate of change is within a preset range. The range determination and trend determination together constitute the individual score for regressing kinematic viscosity.

[0064] For acid value, the same two-dimensional judgment logic as that used for regressing kinematic viscosity is adopted. First, the range is determined based on the average value of multiple consecutive periods and the preset acid value range, and the score is adjusted according to the magnitude of deviation from the range; then, the rate of change between adjacent periods is calculated, and the trend is determined based on the range of the rate of change to form the individual score corresponding to the acid value.

[0065] For pollution levels, the level is determined based on the average value of multiple consecutive periods and a preset pollution level range. Since the pollution level itself is represented in the form of levels, this implementation uses the level range as the basis for judgment, and calculates different scores corresponding to different levels, without separately determining the rate of change.

[0066] For water content and regression conductivity, the interval is determined based on the average value of multiple consecutive periods and the preset interval, and the trend is determined by combining the rate of change between adjacent periods to form the corresponding individual score.

[0067] For temperature, the range is determined by the average value of multiple consecutive periods and the preset temperature range, and the trend is determined by the rate of change, thus forming a single score for temperature.

[0068] For working hours, a time scoring model is constructed based on the cumulative working time and the preset recommended working time. The cumulative working time is obtained by statistically analyzing the working hours corresponding to the collected data, and then compared with the recommended working time. The corresponding score is determined based on the interval in which the cumulative working time falls.

[0069] After determining the individual scores for each item, the scores for regression kinematic viscosity, acid value, contamination level, water content, regression dielectric constant, regression conductivity, temperature, and operating time are weighted and summed according to preset weights to obtain the liquid health score. This score is a unified numerical result used to comprehensively reflect the current state of the liquid.

[0070] By determining the intervals and trends of each parameter separately, and establishing a time-based scoring model in conjunction with working hours, and then performing weighted summation according to preset weights, the liquid health score reflects both the current state of the parameters and their changes over time, thus forming a comprehensive evaluation result with a clear structure and a well-defined calculation path.

[0071] In one exemplary embodiment of this application, one method for determining the remaining service life of a liquid based on its health score and cumulative working time is as follows: a service life correction coefficient is determined based on the interval in which the liquid health score falls; a segmented calculation model is constructed based on the recommended working time, cumulative working time, and the service life correction coefficient to determine the remaining service life of the liquid; when the liquid health score is lower than a preset threshold, the remaining service life of the liquid is determined to be zero.

[0072] First, the lifespan correction factor is determined based on the interval in which the liquid health score falls. The liquid health score is divided into multiple intervals, each corresponding to a different lifespan correction factor. The interval division is consistent with the health score system to ensure that lifespan calculation and health assessment logic are consistent.

[0073] Subsequently, a segmented calculation model was constructed based on the relationship between recommended working time and cumulative working time. Recommended working time serves as the baseline usage time for the liquid, while cumulative working time represents the currently consumed usage time. When the liquid's health score falls within different ranges, the segmented calculation model employs different correction formulas to adjust the difference between the recommended working time and the cumulative working time.

[0074] When the liquid health score is in a high range, a first-type segmented model is constructed based on the recommended working time minus the cumulative working time, combined with a lifespan correction coefficient. When the liquid health score is in the middle range, a second-type segmented model is constructed based on the recommended working time minus the cumulative working time, combined with a lifespan correction coefficient. When the liquid health score is in a low range, a third-type segmented model is used for calculation. When the liquid health score is below a preset threshold, the remaining liquid lifespan is set to zero.

[0075] The calculation factors involved in the segmented calculation model include recommended working time, cumulative working time, and lifespan correction factor. The calculation results form the remaining service life of the liquid and are output in time form.

[0076] This implementation method is based on a liquid health score, which links the health assessment results with the time dimension to keep the remaining service life of the liquid consistent with its current state.

[0077] By introducing different segmented calculation models based on the interval of the liquid health score, the remaining service life of the liquid can be adjusted with changes in health and correspond to the cumulative working time, thereby completing the conversion of health assessment results into time prediction results.

[0078] It should be noted that the health assessment and lifespan prediction method based on liquid characteristic parameters provided in this application can be executed by a health assessment and lifespan prediction system based on liquid characteristic parameters, or by a control module within that system for executing the method. This application uses the execution of the method by a health assessment and lifespan prediction system based on liquid characteristic parameters as an example to illustrate the health assessment and lifespan prediction method based on liquid characteristic parameters provided in this application.

[0079] Reference Figure 2This diagram illustrates a structural schematic of a health assessment and lifespan prediction system based on liquid property parameters, according to an embodiment of this application. The system may specifically include the following modules: The raw parameter acquisition module 21 is used to acquire raw parameter data output by the liquid property sensor. The raw parameter data includes at least temperature, dynamic viscosity, density, dielectric constant, and resistance. The raw parameter filtering module 22 is used to filter the raw parameter data according to a temperature threshold to obtain filtered data; Temperature regression calculation module 23 is used to perform temperature regression calculation based on the filtered data to obtain the regression dielectric constant, regression conductivity and regression kinematic viscosity. Dielectric conductivity calculation module 24 is used to calculate water content, acid value and pollution level based on the regression dielectric constant and the regression conductivity. The health score calculation module 25 is used to perform a weighted calculation based on the regression kinematic viscosity, the acid value, the pollution level, the water content, the regression dielectric constant, the regression conductivity, the temperature, and the working time to determine the liquid health score; The service life calculation module 26 is used to determine the remaining service life of the liquid based on the liquid health score and the cumulative working time.

[0080] In one exemplary embodiment of this application, the temperature regression calculation module 23 includes: The regression dielectric constant determination module is used to construct an exponential function model based on the selected dielectric constant and the corresponding temperature, and to perform temperature correction on the dielectric constant to obtain the regression dielectric constant at the reference temperature.

[0081] In one exemplary embodiment of this application, the temperature regression calculation module 23 includes: The reciprocal relationship model building module is used to establish a reciprocal relationship model between resistance and conductivity based on the selected resistance values. The regression conductivity determination module is used to perform a nonlinear transformation based on the reciprocal relationship model to obtain the regression conductivity at the reference temperature.

[0082] In one exemplary embodiment of this application, the temperature regression calculation module 23 includes: The regression dynamic viscosity determination module is used to perform nonlinear regression calculations based on the screened dynamic viscosity and the corresponding temperature to obtain the regression dynamic viscosity at the reference temperature. The regression kinematic viscosity determination module is used to calculate the regression kinematic viscosity based on the regression dynamic viscosity and the density.

[0083] In one exemplary embodiment of this application, the dielectric conductivity calculation module 24 includes: A moisture content determination module is used to construct an exponential model based on the regression dielectric constant to determine the moisture content. The acid value determination module is used to determine the acid value by constructing a logarithmic model based on the regression conductivity and the regression dielectric constant. The pollution level determination module is used to construct a linear combination model based on the regression conductivity and the regression dielectric constant to determine the pollution level.

[0084] In one exemplary embodiment of this application, the health score calculation module 25 includes: The average value calculation module is used to calculate the average value of the regression kinematic viscosity, the acid value, the pollution level, the water content, the regression conductivity, and the temperature for multiple consecutive periods according to a preset time interval. The single-item score determination module is used to determine the single-item score corresponding to the regression kinematic viscosity by performing interval determination based on the average value of multiple consecutive periods and a preset viscosity range, and by performing trend determination based on the rate of change between adjacent periods; for the acid value, it performs interval determination based on the average value of multiple consecutive periods and a preset acid value range, and by performing trend determination based on the rate of change between adjacent periods; for the pollution level, it performs level determination based on the average value of multiple consecutive periods and a preset pollution level range, and determines the single-item score corresponding to the pollution level; for the water content, it performs determination based on the average value of multiple consecutive periods and a preset water content range. The following methods are used to determine the individual score for the water content: For the regressive conductivity, the method involves determining the individual score based on the average value of multiple consecutive periods and a preset conductivity range, and then determining the individual score based on the rate of change between adjacent periods; for the temperature, the method involves determining the individual score based on the average value of multiple consecutive periods and a preset temperature range, and then determining the individual score based on the rate of change between adjacent periods; and for the working time, a time scoring model is constructed based on the cumulative working time and a preset recommended working time to determine the individual score for the working time. The liquid health score determination module is used to perform a weighted summation of the individual scores according to preset weights to obtain the liquid health score.

[0085] In one exemplary embodiment of this application, the lifespan calculation module 26 includes: The lifespan correction factor determination module is used to determine the lifespan correction factor based on the interval in which the liquid health score falls. The liquid remaining service life determination module is used to construct a segmented calculation model based on the recommended working time, the cumulative working time and the service life correction coefficient to determine the liquid remaining service life. The liquid remaining lifespan determination module is also used to determine the liquid remaining lifespan as zero when the liquid health score is lower than a preset threshold.

[0086] The health assessment and life prediction system based on liquid characteristic parameters in this application embodiment can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, Ultra-Mobile Personal Computers (UMPCs), netbooks, or Personal Digital Assistants (PDAs), etc., while non-mobile electronic devices can be servers, Network Attached Storage (NAS), Personal Computers (PCs), TeleVision (TVs), ATMs, or self-service machines, etc. This application embodiment does not impose specific limitations.

[0087] The health assessment and lifespan prediction system based on liquid property parameters in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit it.

[0088] The health assessment and life prediction system based on liquid property parameters provided in this application can achieve... Figure 1 The method embodiments include various processes for implementing a health assessment and life prediction system based on liquid characteristic parameters, which will not be described again here to avoid repetition.

[0089] Optionally, embodiments of this application also provide an electronic device, including a processor, a memory, and a program or instructions stored in the memory and executable on the processor. When the program or instructions are executed by the processor, they implement the various processes of the above-described embodiments of the health assessment and life prediction method based on liquid characteristic parameters and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0090] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0091] Figure 3 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of this application.

[0092] The electronic device 1000 includes, but is not limited to, components such as: a radio frequency unit 1001, a network module 1002, an audio output unit 1003, an input unit 1004, a sensor 1005, a display unit 1006, a user input unit 1007, an interface unit 1008, a memory 1009, and a processor 1010. The input unit 1004 may include a graphics processor 10041 and a microphone 10042. The display unit 1006 may include a display panel 10061. The user input unit 1007 may include a touch panel 10071 and other input devices 10072. The memory 1009 may include applications and an operating system.

[0093] Those skilled in the art will understand that the electronic device 1000 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 1010 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 3 The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0094] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described embodiments of the health assessment and life prediction method based on liquid characteristic parameters, and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0095] The processor mentioned above is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0096] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and systems in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0097] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0098] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for health assessment and lifespan prediction based on liquid characteristic parameters, characterized in that, The method includes: Acquire raw parameter data output by the liquid property sensor, wherein the raw parameter data includes at least temperature, dynamic viscosity, density, dielectric constant, and resistance. The original parameter data is filtered based on a temperature threshold to obtain filtered data; Temperature regression calculations were performed based on the screened data to obtain the regression dielectric constant, regression conductivity, and regression kinematic viscosity. Calculate the water content, acid value, and pollution level based on the regression dielectric constant and the regression conductivity; The liquid health score is determined by weighting the regression kinematic viscosity, acid value, pollution level, water content, regression dielectric constant, regression conductivity, temperature, and working time. The remaining service life of the liquid is determined based on the liquid health score and the cumulative working time.

2. The method according to claim 1, characterized in that, The step of performing temperature regression calculation based on the screened data to obtain the regression dielectric constant includes: An exponential function model is constructed based on the selected dielectric constant and the corresponding temperature. The dielectric constant is then corrected for temperature to obtain the regression dielectric constant at the reference temperature.

3. The method according to claim 1, characterized in that, The step of performing temperature regression calculation based on the screened data to obtain the regression conductivity includes: Based on the selected resistance values, establish a model showing the reciprocal relationship between resistance and conductivity. The regressed conductivity at the reference temperature is obtained by performing a nonlinear transformation based on the reciprocal relationship model.

4. The method according to claim 1, characterized in that, The step of performing temperature regression calculation based on the screened data to obtain the regressed kinematic viscosity includes: The regression dynamic viscosity at the reference temperature is obtained by performing nonlinear regression calculations based on the selected dynamic viscosity and the corresponding temperature. The regression kinematic viscosity is calculated based on the regression dynamic viscosity and the density.

5. The method according to claim 1, characterized in that, The calculation of water content, acid value, and pollution level based on the regression dielectric constant and the regression conductivity includes: Water content is determined by constructing an exponential model based on the regression dielectric constant. The acid value is determined by constructing a logarithmic model based on the regression conductivity and the regression dielectric constant. The pollution level is determined by constructing a linear combination model based on the regression conductivity and the regression dielectric constant.

6. The method according to claim 1, characterized in that, The liquid health score is determined by weighted calculation based on the regressive kinematic viscosity, acid value, contamination level, water content, regressive dielectric constant, regressive conductivity, temperature, and operating time, including: The average values ​​of the regressive kinematic viscosity, acid value, pollution level, water content, regressive conductivity, and temperature are calculated for multiple consecutive periods according to a preset time interval. For the regressive kinematic viscosity, the interval is determined based on the average value of multiple consecutive periods and the preset viscosity interval, and the trend is determined based on the rate of change between adjacent periods, so as to determine the individual score corresponding to the regressive kinematic viscosity. For the acid value, the interval is determined by comparing the average value of multiple consecutive periods with the preset acid value interval, and the trend is determined by the rate of change between adjacent periods, so as to determine the individual score corresponding to the acid value. For the pollution level, the level is determined by comparing the average value of multiple consecutive periods with a preset pollution level range, and the individual score corresponding to the pollution level is determined. For the moisture content, the interval is determined by comparing the average value of multiple consecutive periods with the preset moisture content interval, and the trend is determined by the rate of change between adjacent periods, so as to determine the individual score corresponding to the moisture content. For the regressed conductivity, the interval is determined by comparing the average value of multiple consecutive periods with the preset conductivity interval, and the trend is determined by the rate of change between adjacent periods, so as to determine the individual score corresponding to the regressed conductivity. For the temperature, the interval is determined by comparing the average value of multiple consecutive periods with the preset temperature range, and the trend is determined by the rate of change between adjacent periods, thus determining the individual score corresponding to the temperature. For the aforementioned working time, a time scoring model is constructed based on the cumulative working time and the preset recommended working time to determine the individual score corresponding to the working time; The scores of each item are weighted and summed according to preset weights to obtain the liquid health score.

7. The method according to claim 1, characterized in that, The process of determining the remaining service life of the liquid based on the liquid health score and cumulative working time includes: The lifespan correction factor is determined based on the interval in which the liquid health score falls. A segmented calculation model is constructed based on the recommended working time, the cumulative working time, and the lifespan correction coefficient to determine the remaining lifespan of the liquid. When the liquid health score is lower than a preset threshold, the remaining service life of the liquid is determined to be zero.

8. A health assessment and lifespan prediction system based on liquid characteristic parameters, characterized in that, The system includes: The raw parameter acquisition module is used to acquire raw parameter data output by the liquid property sensor. The raw parameter data includes at least temperature, dynamic viscosity, density, dielectric constant, and resistance. The raw parameter filtering module is used to filter the raw parameter data according to a temperature threshold to obtain filtered data; The temperature regression calculation module is used to perform temperature regression calculations based on the filtered data to obtain the regression dielectric constant, regression conductivity, and regression kinematic viscosity. The dielectric conductivity calculation module is used to calculate the water content, acid value, and pollution level based on the regression dielectric constant and the regression conductivity. The health score calculation module is used to perform weighted calculations based on the regression kinematic viscosity, the acid value, the pollution level, the water content, the regression dielectric constant, the regression conductivity, the temperature, and the working time to determine the liquid health score; The lifespan calculation module is used to determine the remaining lifespan of the liquid based on the liquid health score and the cumulative working time.

9. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the health assessment and life prediction method based on liquid characteristic parameters as described in any one of claims 1-7.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the health assessment and life prediction method based on liquid characteristic parameters as described in any one of claims 1-7.

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