Method, system and equipment for precisely measuring pour point and condensation point

Abstract

The invention relates to the technical field of pour point and condensation point measurement, in particular to a pour point and condensation point precise measurement method, system and equipment. The method comprises the following steps of: determining a condensation tendency index according to a temperature data sequence fluctuation characteristic, a humidity data sequence change trend and a coupling difference characteristic between temperature and humidity data sequences; a control redundancy index is determined according to the continuous working time of the fan, the condensation tendency index and the numerical deviation characteristics between the power and humidity data sequences; according to the instantaneous change characteristics of adjacent data points in the humidity data sequence, the temperature data sequence and the power data sequence of the current time window, determining a collaborative benefit index; determining a comprehensive evaluation score according to the collaborative benefit index and the control redundancy index; controlling the fan to execute a corresponding operation strategy according to the comprehensive evaluation score and a preset mapping relation between the score and the operation strategy; the operation strategy is repeatedly adjusted until the pour point and the condensation point of the sample are determined, and therefore the fan power responds to environment changes in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of pour point and solidification point measurement, and in particular to a precise pour point and solidification point measurement method, system and equipment. Background Art

[0002] Pour point and cloud point are important indicators of the flow properties of liquids, particularly petroleum products, lubricants, and biodiesel, in low-temperature environments. They directly reflect the liquid's transportability and reliability under low-temperature conditions. As the demand for low-temperature stability continues to increase in various technical fields, such as new energy fuels, bio-based lubricants, and composite additive formulation research, effectively measuring the pour point and cloud point of liquids is crucial.

[0003] At present, most pour point and freezing point measurement equipment uses a cooling chamber to gradually cool the sample, and combines optical, image recognition or mechanical tilting methods to realize the judgment of the low-temperature flow state and crystallization point of the liquid, and then measures the pour point and freezing point of the liquid. However, since the low temperature environment needs to be maintained for a long time during the measurement process of the equipment, condensation water is prone to accumulate in areas such as the cooling chamber, optical window, and image acquisition channel. In severe cases, ice will form on the surface, resulting in obstruction of the light transmission path, image blur, and signal misjudgment, affecting measurement accuracy and stability. Therefore, in order to cope with the condensation and icing phenomenon, most equipment in the existing technology uses a constant power fan to forcibly dissipate the low-temperature airflow and delay the frosting process. However, this method does not take into account the changes in environmental parameters such as humidity and temperature difference in the chamber, which are also crucial factors for the frosting phenomenon, and thus cannot dynamically adjust the fan speed accordingly. This not only leads to redundant energy consumption of the fan and aggravates the aging of the equipment, but also the start and stop of the fan are not matched with the possible frosting phenomenon, reducing the stability of the measurement process. Summary of the Invention

[0004] In order to solve the technical problem that a constant-power fan cannot respond in real time to changes in environmental parameters such as humidity and temperature in the cavity, resulting in redundant energy consumption of the fan and affecting the stability of the measurement process, the present invention provides a precise measurement method, system and equipment for pour point and freezing point. The technical solutions adopted are as follows: The present invention provides a method for accurately measuring pour point and freezing point, which comprises: During the measurement process, the temperature data sequence, humidity data sequence, fan power data sequence and fan continuous working time of the sample cavity are synchronously collected in a preset time window; For each time window, the condensation trend index reflecting the condensation and frosting risk is determined based on the fluctuation characteristics of the temperature data series, the change trend of the humidity data series, and the coupling difference characteristics of the temperature and humidity data series. Determine the control redundancy index that characterizes inefficient or ineffective fan operation in the current time window based on the fan's continuous operating time, condensation trend index, and the numerical deviation characteristics of the power data sequence and humidity data sequence in the current time window at the same time. Based on the instantaneous change characteristics of adjacent data points in the humidity data series, temperature data series, and power data series in the current time window, a synergy benefit index reflecting the degree of synergy between the fan operation strategy and the cavity environment in the current time window is determined; based on the synergy benefit index and the control redundancy index, a comprehensive evaluation score is determined; Based on the comprehensive evaluation score and the preset mapping relationship between the score and the operation strategy, the fan is controlled to execute the corresponding operation strategy; the collection, evaluation and operation strategy adjustment are repeated until the sample pour point and freezing point measurement are completed.

[0005] Furthermore, the process of determining the condensation trend index includes: Obtaining the freezing point reference value of the sample; Calculating the standard deviation of the temperature data sequence; determining the critical condensation index corresponding to the time window based on the standard deviation and the difference characteristics between each temperature value in the temperature data sequence and the freezing point reference value; Based on the normalized fitting error of the temperature data series and the humidity data series and the rate of change of the humidity data series, the temperature and humidity influencing factors corresponding to the time window are determined; Calculate the product of the condensation critical index and the temperature and humidity influencing factors to determine the condensation trend index.

[0006] Furthermore, the process of determining the critical condensation index includes: Calculate the absolute difference between each temperature value and the freezing point reference value as the first difference; Perform arithmetic average calculation on all first differences to obtain the average temperature; The product of the standard deviation and the inverse of the temperature average is calculated as the condensation criticality index.

[0007] Furthermore, the process of determining the temperature and humidity influencing factors includes: Calculate the mean square error between the normalized temperature data series and the humidity data series; Calculate the change rate of adjacent data points in the humidity data series; calculate the average of all change rates to obtain the average change rate; The exponential operation is performed with the natural constant as the base and the average change rate as the exponent to obtain the exponential factor used to amplify the influence of humidity change trend; The product of the mean square error and the exponential factor is calculated as the temperature and humidity influencing factor.

[0008] Furthermore, the control redundancy index determination process includes: Selecting a preset historical time period earlier than the current time window; obtaining condensation trend indicators for each historical time window within the historical time period, and constructing a condensation trend change curve; Calculating the difference between the duration of the historical time period and the continuous working time of the fan in the historical time period as the fan idle time; obtaining the fan energy consumption index through a preset energy consumption index calculation function based on the fan idle time, the slope fluctuation characteristics of the condensation trend change curve, and the average value of all condensation trend indicators in the historical time period; Calculating the absolute difference between the values ​​of the power data sequence and the humidity data sequence at the same time in the current time window as the second difference; performing arithmetic average calculation on all the second differences in the current time window to obtain a first average; A product of the fan energy consumption index and the first average value is calculated as a control redundancy index.

[0009] Furthermore, the process of determining the synergistic benefit index includes: For the current time window, calculate the rate of change of adjacent data points in the temperature data sequence of the current time window; calculate the rate of change of adjacent data points in the humidity data sequence of the current time window; calculate the rate of change of adjacent data points in the power data sequence of the current time window; Arrange all positive change rates in the temperature data sequence in chronological order to form a first change rate sequence; arrange all positive change rates in the humidity data sequence in chronological order to form a second change rate sequence; arrange all positive change rates in the power data sequence in chronological order to form a third change rate sequence; Based on the positive change rates in the same order in the first change rate sequence, the second change rate sequence, and the third change rate sequence, a synergy benefit index is obtained by using a preset synergy benefit index calculation function.

[0010] Furthermore, the comprehensive assessment score determination process includes: The geometric mean between the synergy benefit index and the reciprocal of the control redundancy index is normalized to obtain the comprehensive evaluation score.

[0011] Furthermore, the preset score and operation strategy mapping relationship includes three preset score intervals and corresponding operation strategies, namely, high-efficiency interval: when the comprehensive evaluation score is in the high-efficiency interval, the fan's current operation strategy is maintained; optimization interval: when the comprehensive evaluation score is in the optimization interval, the fan power is started to be fine-tuned linearly; reconstruction interval: when the comprehensive evaluation score is in the reconstruction interval, the fan control logic is reconstructed and the fan speed strategy is set in stages; The step of controlling the fan to execute the corresponding operation strategy based on the comprehensive evaluation score and the preset mapping relationship between the score and the operation strategy includes: Determine the target score range for the comprehensive assessment score; Based on the operation strategy corresponding to the target score range, the fan is triggered to perform corresponding control actions.

[0012] A precise measurement system for pour point and freezing point, comprising: An acquisition module is used to synchronously acquire the temperature data sequence, humidity data sequence, fan power data sequence and fan continuous working time of the sample cavity in a preset time window during the measurement process; A first determination module is configured to determine, for each time window, a condensation trend indicator reflecting the risk of condensation and frosting based on the fluctuation characteristics of the temperature data sequence, the change trend of the humidity data sequence, and the coupling difference characteristics of the temperature data sequence and the humidity data sequence; The second determination module is configured to determine a control redundancy index representing inefficient or ineffective operation of the fan in the current time window based on the continuous operating time of the fan, the condensation trend index, and the numerical deviation characteristics of the power data sequence and the humidity data sequence in the current time window at the same time; A third determination module is configured to determine a synergy benefit index reflecting the degree of synergy between the fan operation strategy and the cavity environment in the current time window based on the instantaneous change characteristics of adjacent data points in the humidity data sequence, temperature data sequence, and power data sequence in the current time window; and determine a comprehensive evaluation score based on the synergy benefit index and the control redundancy index; The operation strategy module is used to control the fan to execute the corresponding operation strategy based on the comprehensive evaluation score and the preset mapping relationship between the score and the operation strategy; repeat the collection, evaluation and operation strategy adjustment until the sample pour point and freezing point measurement are completed.

[0013] A precision measurement device for pour point and solidification point comprises a memory, a processor and a computer program stored in the memory and running on the processor. When the processor executes the computer program, steps of a precision measurement method for pour point and solidification point are implemented.

[0014] The present invention has the following beneficial effects: The present invention uses condensation trend indicators to predict condensation and frosting risk points in real time, so as to facilitate the subsequent dynamic adjustment of fan power, thereby avoiding the decrease in transmittance of the optical window of the measuring equipment due to ice or frost and image recognition, and reducing the pour point and freezing point measurement error rate; secondly, by controlling the redundancy indicator to identify the time period of ineffective operation of the fan, and analyzing the dynamic matching relationship between fan power and temperature and humidity, the fan power can be adjusted based on the different stages of the measuring equipment to reduce inefficient energy consumption; then, based on the comprehensive evaluation score, the adjustment of the fan operation strategy is triggered, thereby improving the robustness of the measuring equipment to complex environments, such as complex environments with large temperature fluctuations and sudden changes in humidity, thereby extending the life of key components in the measuring equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 A flow chart of a method for accurately measuring pour point and freezing point provided by one embodiment of the present invention; Figure 2 An example diagram of a temperature data curve, a humidity data curve, and a power data curve provided by an embodiment of the present invention; Figure 3 An example diagram of a condensation trend indicator determination process provided by one embodiment of the present invention; Figure 4 This is an example diagram of the control redundancy index determination process provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0017] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, provides a detailed description of a method, system, and apparatus for precisely measuring pour and pour points according to the present invention, including its specific implementation, structure, features, and effectiveness. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The specific scheme of the precise measurement method, system and equipment of pour point and freezing point provided by the present invention is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of a method for accurately measuring pour point and freezing point provided by one embodiment of the present invention, the method comprising: S101: During the measurement process, a temperature data sequence, a humidity data sequence, a fan power data sequence, and a fan continuous working time of the sample cavity are synchronously collected in a preset time window.

[0021] It should be noted that the specific information of the preset time window can be determined according to actual needs, and this embodiment does not make any specific limitations. For example, the preset time window can be a rolling time window with a fixed length of 1 minute.

[0022] In this embodiment, within each time window, a unified clock reference (such as a high-precision crystal oscillator) and a unified sampling frequency (such as once every 1 second) are used to collect the temperature value, humidity value and fan power value of the sample cavity.

[0023] It should be noted that the sampling frequency is dynamically adjusted according to the measurement stage. For example, in the pre-cooling stage: the sampling frequency is reduced (for example, 30 seconds / time) to reduce data redundancy; in the critical stage (close to the estimated freezing point): the sampling frequency is increased (for example, 1 second / time) to capture the mutation point.

[0024] It can be understood that the temperature data sequence consists of temperature values ​​arranged in chronological order; the humidity data sequence consists of humidity values ​​arranged in chronological order; and the power data sequence consists of power values ​​arranged in chronological order.

[0025] It should be noted that in order to facilitate the subsequent direct analysis of the temperature data sequence, humidity data sequence, and fan power data sequence, it is necessary to align the temperature data sequence, humidity data sequence, and fan power data sequence in time. Therefore, the timestamp accuracy of the data points in each data sequence needs to reach the millisecond level.

[0026] It should be noted that the specific material of the sample is not specifically limited in this embodiment, for example, the sample is petroleum.

[0027] For example, the process of collecting the temperature data sequence, humidity data sequence, and fan power data sequence of the sample cavity includes the following common methods: first, check whether key components such as the temperature sensor, humidity sensor, optical channel, image acquisition module, tilt motor, and fan are normal; the refrigeration unit starts to operate, and various sensors (temperature sensor, humidity sensor) are used to synchronously collect temperature data, humidity data, and fan power data in the cavity within a preset time window, and support data caching within the time window with a timestamp; every time the temperature drops by a set value or every time period, start optical path acquisition, record the photoelectric signal intensity value, align the current temperature with the light intensity data, and record it in the "optical transmittance change table" to determine whether it has entered the transmittance mutation zone (as a freezing point criterion); when the temperature drops to the detection point, drive the tilt mechanism to tilt the sample tube, and start the image acquisition module or motion sensor to monitor whether the liquid is flowing; record the action execution time and tilt response signal, and then store the tilt response result; all types of data (temperature, humidity, fan power, etc.) are uniformly timestamped to construct a data sequence.

[0028] It should be noted that, while ensuring that the cooling effect of the precision measuring device of the pour point and freezing point is not affected on the sample, various sensors should be placed as close to key components as possible so that temperature data, humidity data and fan power data can be accurately collected. For example, a humidity sensor (such as DHT22 / AM2302) can be installed on the equipment housing, light window or cavity to obtain real-time humidity change data during the measurement process.

[0029] It should be noted that in order to eliminate the impact of differences in dimensions and value ranges among the various types of data collected, it is necessary to normalize the various types of data, that is, to normalize the collected temperature values, humidity values, and fan power values.

[0030] Among them, it should be noted that in order to better analyze the correlation between the temperature data sequence, humidity data sequence and fan power data sequence in each time window, we can first construct a temperature data curve based on the collected temperature data sequence, construct a humidity data curve based on the collected humidity data sequence, and construct a power data curve based on the collected power data sequence. Then, the temperature data curve, humidity data curve and power data curve in each time window are expressed in the same coordinate system, so as to better analyze the correlation between temperature changes, humidity changes and fan power changes during the measurement process.

[0031] For example, within a certain time window, the temperature data curve, humidity data curve and power data curve are as follows: Figure 2 As shown, Figure 2The horizontal axis is time (unit: seconds), the vertical axis is the normalized value, the dotted line represents the humidity data curve, the thin solid line represents the temperature data curve, and the thick solid line represents the power data curve.

[0032] Fan continuous working time, which indicates the cumulative running time of the fan.

[0033] S102: For each time window, determine a condensation trend indicator reflecting the condensation and frosting risk based on the fluctuation characteristics of the temperature data sequence, the change trend of the humidity data sequence, and the coupling difference characteristics of the temperature data sequence and the humidity data sequence.

[0034] The process of determining the condensation trend index is as follows: Figure 3 Shown, including: S102-1: Obtain a freezing point reference value of the sample.

[0035] Among them, it should be noted that in order to avoid a large deviation between the freezing point reference value and the actual value (such as inaccurate reference value of unknown samples), which will lead to distortion in the calculation of the condensation trend index, a dynamic correction mechanism for the "freezing point reference value" is added. For example, "during the measurement process, the freezing point reference value is dynamically updated according to the deviation between the real-time temperature data sequence and the optical / mechanical detection signal" to avoid logical defects caused by relying on static reference values.

[0036] It should be noted that, since the collected temperature values ​​are normalized, the specific value of the freezing point reference value is also a normalized value.

[0037] It should be noted that the reference value of the freezing point of the sample is determined based on the material of the sample to be tested and the public knowledge in this technical field. For example, if the material of the sample is No. 5 diesel, it is known that the freezing point of No. 5 diesel is required to be no higher than 5°C. The value obtained after normalizing the temperature value of 5°C is the reference value of the freezing point.

[0038] S102-2: Calculate the standard deviation of the temperature data sequence; determine the critical condensation index corresponding to the time window based on the standard deviation and the difference characteristics between each temperature value in the temperature data sequence and the freezing point reference value.

[0039] Standard deviation, characterizes the fluctuation of temperature values.

[0040] It should be noted that the specific calculation method of the standard deviation is a technical means well known to those skilled in the art and will not be described in detail in this embodiment.

[0041] In this embodiment, the absolute difference between each temperature value and the freezing point reference value is calculated as the first difference; all the first differences are arithmetic averaged to obtain the temperature average; and the product of the standard deviation and the reciprocal of the temperature average is calculated as the condensation critical index.

[0042] The average temperature is used to reflect the overall fluctuation of the temperature data series.

[0043] The condensation critical indicator is used to determine whether the temperature is in an unstable state that is prone to condensation. For example, when the temperature drops suddenly, the risk of condensation increases significantly.

[0044] Since, if the average temperature in a certain time window is smaller, it means that the difference between the temperature value and the sample's freezing point reference value is smaller, reflecting that condensation is more likely to occur, and if the temperature fluctuation in the time window is larger, the condensation freezing phenomenon will be aggravated. Therefore, assuming i=2, 3, ..., n, i is a positive integer, the condensation critical index can be expressed by the following formula: in, represents the critical index of condensation in the cth time window; Indicates the reference value of the freezing point of the sample; represents the i-th temperature value in the temperature data sequence within the c-th time window; represents the standard deviation of the temperature data sequence in the cth time window; n represents the total number of data points in the temperature data sequence in the cth time window.

[0045] Among them, it should be noted that is the theoretical reference value of the sample freezing point. In actual working conditions, the temperature in the cavity is constantly changing during the measurement process. It is impossible for all the collected temperature values ​​to be equal to the freezing point reference value, so the average temperature value cannot be zero, that is, It can't be zero.

[0046] S102-3: Determine the temperature and humidity influencing factors corresponding to the time window based on the normalized fitting errors of the temperature data sequence and the humidity data sequence and the rate of change of the humidity data sequence.

[0047] Normalized fitting error refers to the standardized fitting deviation between the temperature and humidity data series after mapping them to the same dimensionless coordinate system. Its core purpose is to eliminate the dimensional differences in the original temperature and humidity data, and through data normalization, enable trend comparison and analysis on the same scale.

[0048] It should be noted that the mean square error (MSE) can be used. ) is used to quantify the error between the two normalized curves. The specific calculation method of the mean square error is well known to those skilled in the art and will not be described in detail in this embodiment.

[0049] In this embodiment, the mean square error between the normalized temperature data sequence and the humidity data sequence is calculated; the rate of change of adjacent data points in the humidity data sequence is calculated; the average of all change rates is calculated to obtain the average change rate; an exponential operation is performed with a natural constant as the base and the average change rate as the exponent to obtain an exponential factor for amplifying the impact of the humidity change trend; the product of the mean square error and the exponential factor is calculated as the temperature and humidity influencing factor.

[0050] It should be noted that the specific calculation method of the rate of change is a method well known to those skilled in the art and will not be described in detail in this embodiment. For example, assuming that the humidity data sequence (humidity values ​​arranged in chronological order, [ , ,…, ]), focusing on the change relationship between two adjacent data points (such as and ), for two adjacent humidity data points (such as and ), first get the time interval between the two (Right now timestamp minus timestamp), and then calculate the humidity difference , and finally through the rate of change = Get two adjacent humidity data points ( and ), where Indicates the first humidity value in the humidity data sequence; Represents the second humidity value in the humidity data sequence; S represents the total number of data points in the humidity data sequence.

[0051] The rate of change reflects the trend of humidity change. If the rate of change is positive, it indicates that the humidity is gradually increasing, and the larger the value, the faster the increase; if the rate of change is negative, it indicates that the humidity is decreasing; if it is close to 0, it indicates that the humidity is basically stable.

[0052] The exponential factor is a nonlinear weighting of the humidity change trend. If K (average change rate) is greater than 0, then If it is greater than 1, the risk of condensation will be significantly amplified, that is, the faster the humidity rises, the greater the risk of condensation and frosting.

[0053] S102-4: Calculate the product of the condensation critical index and the temperature and humidity influencing factor to determine the condensation trend index.

[0054] Since, if the temperature and humidity influence factor is larger within a certain time window, it means that the trend difference between temperature change and humidity change is larger, and the humidity may show a gradually increasing trend; then for the condensation and frosting phenomenon, the higher the temperature, the greater the possibility of condensation and freezing; and if the condensation critical index is larger, it means that the temperature fluctuation is larger within the time window, the difference between the temperature value and the sample's freezing point reference value is smaller, and the condensation and frosting phenomenon surges. Therefore, the condensation trend index can be expressed by the following formula: in, represents the condensation trend indicator of the cth time window; Indicates the critical index of condensation; represents the mean square error; represents the exponential factor; K represents the average rate of change.

[0055] S103: Determine a control redundancy index representing inefficient or ineffective fan operation in the current time window based on the fan continuous working time, the condensation trend index, and the numerical deviation characteristics of the power data sequence and the humidity data sequence in the current time window at the same time.

[0056] It should be understood that by collecting environmental parameters such as temperature and humidity in the measurement chamber, the condensation trend index is determined. Therefore, based on the condensation trend index, dynamic identification of condensation and frosting phenomena during the measurement process is achieved, thereby providing a feedforward decision-making basis for fan control. However, if the fan operation strategy only relies on the identification results of the occurrence of condensation and frosting phenomena, the fan may be frequently started and stopped or run continuously for a long time due to frequent risk fluctuations or unclear feature identification boundaries, thereby causing control redundancy, resulting in increased equipment energy consumption and affecting the stability of the measurement process. Therefore, it is necessary to further analyze the correlation between the condensation trend index and the fan's energy consumption.

[0057] The process of determining the control redundancy index is as follows: Figure 4 Shown, including: S103-1: Selecting a preset historical time period earlier than the current time window; obtaining condensation trend indicators of each historical time window within the historical time period, and constructing a condensation trend change curve.

[0058] It should be noted that the specific value of the preset historical time period is determined according to actual needs and is not specifically limited in this embodiment. For example, it is 5 minutes earlier than the current time window.

[0059] Among them, it should be noted that, when selecting the preset historical time period, considering the subsequent analysis of the correlation between the condensation trend indicator and the fan energy consumption, the preset historical time period should include an appropriate number of time windows as much as possible. For example, the preset historical time period is 5 minutes before the current time window, and the fixed length of the time window is 1 minute, then the historical time period contains 5 historical time windows.

[0060] A historical time window is a unit of time used to store and analyze historical data within a fixed or dynamically sliding period during device measurement. This is a retrospective analysis dimension relative to the "current time window" (for example, a one-minute window for real-time measurement). The historical time window uses a caching mechanism to store key data (such as temperature, humidity, power, and condensation trend indicators).

[0061] Among them, it should be noted that the specific construction method of constructing the condensation trend change curve can refer to the explanation of the method of constructing the temperature data curve, humidity data curve and power data curve in step S101, and this embodiment will not go into details. For example, the condensation trend indicators of each historical time window in the historical time period are arranged in chronological order to form a condensation trend indicator sequence, and then the condensation trend indicator sequence is mapped to a dimensionless coordinate system. The dimensionless coordinate system can be a two-dimensional rectangular coordinate system. The condensation trend change curve is displayed in the two-dimensional rectangular coordinate system, where the horizontal axis is time and the vertical axis is the condensation trend indicator.

[0062] S103-2: Calculate the difference between the length of the historical time period and the continuous working time of the fan in the historical time period as the fan idle time; based on the fan idle time, the slope fluctuation characteristics of the condensation trend change curve, and the average value of all condensation trend indicators in the historical time period, obtain the fan energy consumption index through a preset energy consumption index calculation function.

[0063] In this embodiment, the slopes of adjacent data points on the condensation trend change curve are calculated as the first slope; the absolute difference between each first slope is calculated as the slope difference; all slope differences are arithmetic averaged to obtain the slope average; the product of the fan idle time, the slope average and the average of all condensation trend indicators in the historical time period is calculated as the stability index; the inverse of the sum of the stability index and the minimum positive offset is used as the energy consumption index of the fan.

[0064] It should be noted that the specific method of calculating the slope is a technical means well known to those skilled in the art and will not be described in detail in this embodiment.

[0065] Since, if the stability index is small within the preset historical time period, it means that the trend of changes between the various condensation change indicators is relatively stable, and combined with the condensation change index, it can be understood that within the preset historical time period, the condensation trend index changes smoothly and the overall level is low, reflecting that the overall condensation phenomenon is low. However, if the fan idle time is short, it means that the fan continues to work for a long time, and the fan energy consumption is also high in this process. Therefore, the fan energy consumption index can be expressed by the following preset energy consumption index calculation function: Among them, F represents the energy consumption index of the fan; Indicates the average value of all condensation trend indicators in the historical period; Indicates the fan idle time; It represents the absolute difference between the first slopes of any two groups of data in the condensation trend change curve, that is, the slope difference, wherein any group of adjacent data points is a group of data, and a group of data contains the first slopes between adjacent data points; assuming that there are y groups of adjacent data points on the condensation trend change curve, there are y groups of data, and y groups of data contain y first slopes (y is a positive integer), m represents the total number of slope differences in the condensation trend change curve, then It represents the b-th slope difference among the m slope differences of the condensation trend change curve, where b is a positive integer and is less than or equal to m.

[0066] It should be noted that if the preset historical time period is still in the measurement process, the temperature, humidity and fan power in the device will also change continuously, so the condensation trend index of each historical time window cannot be zero. It is impossible to be zero. It can be seen that the preset historical time period contains an appropriate amount of time windows. Under actual working conditions, even if the changes in the condensation trend indicators are very stable, there will be slight changes in temperature and humidity, so the slope difference cannot be zero.

[0067] It should be noted that in the denominator Add a very small positive offset to , mathematically ensuring that the denominator Strictly greater than zero, thus completely eliminating the possibility of the denominator being zero in the division operation, ensuring the robustness and executable nature of the calculation formula, and It is preset to a sufficiently small positive value, and the principle for selecting its specific value is: Under normal operating conditions, it is significantly greater than zero. The influence on the calculated result F can be ignored, for example, The specific value of is determined according to actual needs. It can be 0.01, which is not specifically limited in this embodiment.

[0068] S103-3: Calculate the absolute difference between the values ​​of the power data sequence and the humidity data sequence at the same time in the current time window as the second difference; perform arithmetic average calculation on all the second differences in the current time window to obtain a first average.

[0069] The first average value is used to quantify the response delay between power data changes and humidity data fluctuations within the current time window. The response delay between power data changes and humidity data fluctuations can be used to determine inefficient or ineffective fan operation. Inefficient or ineffective fan operation can fall into the following two categories: one is that fan power changes significantly lag behind humidity fluctuations; the other is that the fan continues to operate at high power even after the risk of condensation has disappeared.

[0070] S103 - 4 : Calculate the product of the fan energy consumption index and the first average value as a control redundancy index.

[0071] It should be understood that if the first average value is larger within the current time window, it reflects that the response delay between humidity changes and power changes is more serious, which means that as the humidity changes, the delayed reaction time of the fan power changes accordingly is longer, which reflects that the fan may have redundant control behavior; and if the fan energy consumption change index is larger, it reflects that within the current time window, the fan consumes more energy, and the fan redundant behavior is more significant.

[0072] S104: Determine a synergy benefit index reflecting the degree of coordination between the fan operation strategy and the cavity environment in the current time window based on the instantaneous change characteristics of adjacent data points in the humidity data sequence, temperature data sequence, and power data sequence in the current time window; and determine a comprehensive evaluation score based on the synergy benefit index and the control redundancy index.

[0073] It is important to understand that after the fan operation behavior is determined to be potentially redundant, it is further necessary to introduce the task rhythm (i.e., the dynamic temperature changes at each stage of the measurement process) and observe how the fan should be more accurately coordinated under different temperature changes to match the stage differences in the condensation phenomenon during the measurement process, so as to achieve coordinated adjustment of the fan operation strategy and trigger the optimal operation strategy under the current task rhythm.

[0074] In this embodiment, for the current time window, the change rate of adjacent data points in the temperature data sequence of the current time window is calculated; the change rate of adjacent data points in the humidity data sequence of the current time window is calculated; the change rate of adjacent data points in the power data sequence of the current time window is calculated; all positive change rates in the temperature data sequence are arranged in chronological order to form a first change rate sequence; all positive change rates in the humidity data sequence are arranged in chronological order to form a second change rate sequence; all positive change rates in the power data sequence are arranged in chronological order to form a third change rate sequence; based on the positive change rates in the same order in the first change rate sequence, the second change rate sequence and the third change rate sequence, a synergistic benefit index is obtained by presetting a synergistic benefit index calculation function.

[0075] Since, if the absolute difference between the positive change rates in the same order in the second change rate sequence and the first change rate sequence is larger, it means that in the current time window, the trend difference between the temperature change and the humidity change is large, the humidity is showing an upward trend, and the temperature is showing a downward trend. At this time, the fan power needs to be increased; and if the absolute difference between the positive change rates in the same order in the second change rate sequence and the third change rate sequence is smaller, it means that as the humidity and temperature change, the fan's response efficiency is higher in response to environmental changes, thereby reflecting that the fan's current operation strategy is better. Therefore, the synergistic benefit index can be expressed by the following formula: in, represents the synergistic benefit index of the current time window; Represents a function that returns the average value; represents the total number of data points in the second rate of change series; represents the vth positive rate of change in the second rate of change sequence; represents the vth positive rate of change in the first rate of change sequence; represents the vth positive rate of change in the third rate of change sequence; | | represents the absolute value.

[0076] It should be noted that under actual working conditions, even if the humidity rises sharply at a certain moment, the fan has a high response efficiency at this time, and takes away the generated cold air in time during the measurement process, so that the humidity in the equipment does not surge at that moment. However, the fan cannot completely bring in the surge in cold air, so the humidity in the equipment may still rise slightly at that moment. Therefore, in the current time window, the number of positive change rates cannot be zero.

[0077] It should be noted that although the humidity data sequence, power data sequence, and temperature data sequence have the same number of data points due to the synchronized timestamps carried, it is not possible to guarantee that the number of positive change rates in each data sequence is also consistent. Therefore, if there is no v-th positive change rate in the first change rate sequence or the third change rate sequence, then set is zero.

[0078] In this embodiment, the geometric mean of the synergy benefit index and the reciprocal of the control redundancy index is normalized to obtain a comprehensive evaluation score.

[0079] It should be noted that the specific calculation method of the geometric mean is a technical means well known to those skilled in the art and will not be repeated in this embodiment. For example, the calculation method of the geometric mean between the synergy benefit index and the reciprocal of the control redundancy index can be expressed as: ,in, represents the synergistic benefit index, and Q represents the control redundancy index.

[0080] It should be noted that under actual working conditions, even with changes in humidity and temperature, in order to respond to environmental changes and control the fan to respond in a timely manner, due to interference from various objective factors such as signal delay, the change in fan power cannot completely match the change in humidity. This shows that there will always be some non-negligible redundant behaviors during the operation of the fan, and Q cannot be zero.

[0081] It's important to understand that after calculating the fan coordination benefit index, it can be further integrated with the aforementioned fan control redundancy index. The fan coordination benefit index reflects the adaptability of the fan response to the task rhythm, while the fan control redundancy index reveals the level of resource redundancy in the fan operation behavior. Together, these two indicators describe the rationality of the current operation strategy from the perspectives of "benefit" and "resource consumption," respectively.

[0082] S105: Based on the comprehensive evaluation score and the preset mapping relationship between the score and the operation strategy, the fan is controlled to execute the corresponding operation strategy; the collection, evaluation and operation strategy adjustment are repeated until the pour point and freezing point measurement of the sample are completed.

[0083] Among them, it should be noted that the preset score and operation strategy mapping relationship includes three preset score intervals and corresponding operation strategies, namely, high-efficiency interval: when the comprehensive evaluation score is in the high-efficiency interval, maintain the current operation strategy of the fan; optimization interval: when the comprehensive evaluation score is in the optimization interval, start the fan power linear increase fine-tuning; reconstruction interval: when the comprehensive evaluation score is in the reconstruction interval, reconstruct the fan control logic and set the fan wind speed strategy in stages.

[0084] In this embodiment, the target score range in which the comprehensive evaluation score falls is determined; and based on the operation strategy corresponding to the target score range, the fan is triggered to perform a corresponding control action.

[0085] For example, the mapping relationship between the preset score and the operation strategy is shown in Table 1. Assuming that the comprehensive evaluation score is 0.75 and 0.75 is in the optimization range, the optimization range is the target score range. Based on the operation strategy corresponding to the target score range (optimization range), the fan is triggered to perform linear power increase fine-tuning.

[0086] Table 1 It should be noted that starting the fan power linearly and incrementally fine-tuning means gradually increasing the output power based on the current power value according to the preset step size (such as 5W / minute) until the comprehensive evaluation score returns to the high-efficiency range or reaches the power upper limit (such as 50W).

[0087] It should be noted that reconstructing the fan control logic refers to the process of regenerating control parameters such as fan speed, power, start and stop rules based on real-time collected vibration spectrum, bearing temperature and other data. This process is a technical means well known to those skilled in the art and will not be repeated in this embodiment; setting the fan speed strategy in stages refers to dynamically adjusting the fan speed based on the dynamic temperature changes in each stage. For example, in the initialization stage (just starting to cool down), the fan is controlled to run at the lowest safe speed (such as 800rpm).

[0088] It should be noted that repeated collection, evaluation and operation strategy adjustment refers to the process of "controlling the fan to execute the corresponding operation strategy based on the comprehensive evaluation score and the preset score and operation strategy mapping relationship" in steps S101 to S105. The specific explanation will not be repeated in this embodiment.

[0089] An embodiment of the present invention provides a precise measurement system for pour point and freezing point, the system comprising: an acquisition module for synchronously acquiring a temperature data sequence, a humidity data sequence, a fan power data sequence, and a continuous working time of a sample cavity in a preset time window during the measurement process; a first determination module for determining, for each time window, a condensation trend index reflecting the risk of condensation and frosting based on the fluctuation characteristics of the temperature data sequence, the change trend of the humidity data sequence, and the coupling difference characteristics of the temperature data sequence and the humidity data sequence; a second determination module for determining, based on the continuous working time of the fan, the condensation trend index, and the power data sequence and humidity data sequence in the current time window, the condensation trend index reflecting the risk of condensation and frosting. The numerical deviation characteristics at the same time are used to determine the control redundancy index of the current time window that characterizes the inefficient or ineffective operation of the fan; the third determination module is used to determine the synergy benefit index of the current time window that reflects the degree of coordination between the fan operation strategy and the cavity environment based on the instantaneous change characteristics of the adjacent data points in the humidity data sequence, temperature data sequence and power data sequence in the current time window; the comprehensive evaluation score is determined based on the synergy benefit index and the control redundancy index; the operation strategy module is used to control the fan to execute the corresponding operation strategy based on the comprehensive evaluation score and the preset mapping relationship between the score and the operation strategy; repeat the collection, evaluation and operation strategy adjustment until the sample pour point and freezing point measurement are completed.

[0090] An embodiment of the present invention provides a precision measurement device for pour point and solidification point. The device includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of a precision measurement method for pour point and solidification point are implemented.

[0091] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0092] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for accurately measuring pour point and freezing point, characterized in that: The method comprises: During the measurement process, the temperature data sequence, humidity data sequence, fan power data sequence and fan continuous working time of the sample cavity are synchronously collected in a preset time window; For each time window, the condensation trend index reflecting the condensation and frosting risk is determined based on the fluctuation characteristics of the temperature data series, the change trend of the humidity data series, and the coupling difference characteristics of the temperature and humidity data series. Determine the control redundancy index that characterizes inefficient or ineffective fan operation in the current time window based on the fan's continuous operating time, condensation trend index, and the numerical deviation characteristics of the power data sequence and humidity data sequence in the current time window at the same time. Based on the instantaneous change characteristics of adjacent data points in the humidity data series, temperature data series, and power data series in the current time window, a synergy benefit index reflecting the degree of synergy between the fan operation strategy and the cavity environment in the current time window is determined; based on the synergy benefit index and the control redundancy index, a comprehensive evaluation score is determined; Based on the comprehensive evaluation score and the preset mapping relationship between the score and the operation strategy, the fan is controlled to execute the corresponding operation strategy; the collection, evaluation and operation strategy adjustment are repeated until the sample pour point and freezing point measurement are completed.

2. The method for accurately measuring pour point and freezing point according to claim 1, wherein: The condensation trend index determination process includes: Obtaining the freezing point reference value of the sample; Calculating the standard deviation of the temperature data sequence; determining the critical condensation index corresponding to the time window based on the standard deviation and the difference characteristics between each temperature value in the temperature data sequence and the freezing point reference value; Based on the normalized fitting error of the temperature data series and the humidity data series and the rate of change of the humidity data series, the temperature and humidity influencing factors corresponding to the time window are determined; Calculate the product of the condensation critical index and the temperature and humidity influencing factors to determine the condensation trend index.

3. The method for accurately measuring pour point and freezing point according to claim 2, wherein: The condensation critical index determination process includes: Calculate the absolute difference between each temperature value and the freezing point reference value as the first difference; Perform arithmetic average calculation on all first differences to obtain the average temperature; The product of the standard deviation and the inverse of the temperature average is calculated as the condensation criticality index.

4. The method for accurately measuring pour point and freezing point according to claim 2, wherein: The temperature and humidity influencing factor determination process includes: Calculate the mean square error between the normalized temperature data series and the humidity data series; Calculate the change rate of adjacent data points in the humidity data series; calculate the average of all change rates to obtain the average change rate; The exponential operation is performed with the natural constant as the base and the average change rate as the exponent to obtain the exponential factor used to amplify the influence of humidity change trend; The product of the mean square error and the exponential factor is calculated as the temperature and humidity influencing factor.

5. The method for accurately measuring pour point and freezing point according to claim 1, wherein: The control redundancy index determination process includes: Selecting a preset historical time period earlier than the current time window; obtaining condensation trend indicators for each historical time window within the historical time period, and constructing a condensation trend change curve; Calculating the difference between the duration of the historical time period and the continuous working time of the fan in the historical time period as the fan idle time; obtaining the fan energy consumption index through a preset energy consumption index calculation function based on the fan idle time, the slope fluctuation characteristics of the condensation trend change curve, and the average value of all condensation trend indicators in the historical time period; Calculating the absolute difference between the values ​​of the power data sequence and the humidity data sequence at the same time in the current time window as the second difference; performing arithmetic average calculation on all the second differences in the current time window to obtain a first average; A product of the fan energy consumption index and the first average value is calculated as a control redundancy index.

6. The method for accurately measuring pour point and freezing point according to claim 5, wherein: The process of determining the synergistic benefit index includes: For the current time window, calculate the rate of change of adjacent data points in the temperature data sequence of the current time window; calculate the rate of change of adjacent data points in the humidity data sequence of the current time window; calculate the rate of change of adjacent data points in the power data sequence of the current time window; Arrange all positive change rates in the temperature data sequence in chronological order to form a first change rate sequence; arrange all positive change rates in the humidity data sequence in chronological order to form a second change rate sequence; arrange all positive change rates in the power data sequence in chronological order to form a third change rate sequence; Based on the positive change rates in the same order in the first change rate sequence, the second change rate sequence, and the third change rate sequence, a synergy benefit index is obtained by using a preset synergy benefit index calculation function.

7. The method for accurately measuring pour point and freezing point according to claim 6, wherein: The comprehensive assessment score determination process includes: The geometric mean between the synergy benefit index and the reciprocal of the control redundancy index is normalized to obtain the comprehensive evaluation score.

8. The method for accurately measuring pour point and freezing point according to claim 1, wherein: The preset score and operation strategy mapping relationship includes three preset score intervals and corresponding operation strategies, namely, high efficiency interval: when the comprehensive evaluation score is in the high efficiency interval, maintain the current operation strategy of the fan; optimize Range: When the comprehensive evaluation score is in the optimization range, the fan power is linearly increased and fine-tuned; reconstruction Interval: When the comprehensive evaluation score is in the reconstruction interval, the fan control logic is reconstructed and the fan speed strategy is set in stages; The step of controlling the fan to execute the corresponding operation strategy based on the comprehensive evaluation score and the preset mapping relationship between the score and the operation strategy includes: Determine the target score range for the comprehensive assessment score; Based on the operation strategy corresponding to the target score range, the fan is triggered to perform corresponding control actions.

9. A precise measurement system for pour point and freezing point, characterized in that: The system comprises: An acquisition module is used to synchronously acquire the temperature data sequence, humidity data sequence, fan power data sequence and fan continuous working time of the sample cavity in a preset time window during the measurement process; A first determination module is configured to determine, for each time window, a condensation trend indicator reflecting the risk of condensation and frosting based on the fluctuation characteristics of the temperature data sequence, the change trend of the humidity data sequence, and the coupling difference characteristics of the temperature data sequence and the humidity data sequence; The second determination module is configured to determine a control redundancy index representing inefficient or ineffective operation of the fan in the current time window based on the continuous operating time of the fan, the condensation trend index, and the numerical deviation characteristics of the power data sequence and the humidity data sequence in the current time window at the same time; A third determination module is configured to determine a synergy benefit index reflecting the degree of synergy between the fan operation strategy and the cavity environment in the current time window based on the instantaneous change characteristics of adjacent data points in the humidity data sequence, temperature data sequence, and power data sequence in the current time window; and determine a comprehensive evaluation score based on the synergy benefit index and the control redundancy index; The operation strategy module is used to control the fan to execute the corresponding operation strategy based on the comprehensive evaluation score and the preset mapping relationship between the score and the operation strategy; repeat the collection, evaluation and operation strategy adjustment until the sample pour point and freezing point measurement are completed.

10. A precision measuring device for pour point and freezing point, characterized in that: The device includes a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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