Method, device and storage medium for detecting density of wine flowing in a pipe

Abstract

The application provides a kind of pipe in flowing liquor density detection method, equipment and storage medium, belong to liquid density detection technical field.The method comprises: obtaining vibration frequency and driving energy consumption value under different fluid resistance conditions, according to the density of calibration liquid, form mapping data set;According to mapping data set, establish multidimensional calibration relationship;Multidimensional calibration relationship represents the mapping rule between vibration frequency and true density under different fluid resistance;When normal operation, obtain real-time vibration frequency and real-time driving energy consumption value;When determining that real-time vibration frequency contains the deviation caused by fluid resistance, then fluid resistance compensates real-time vibration frequency;According to the real-time vibration frequency after compensation, real-time driving energy consumption value and multidimensional calibration relationship, determine the final density.It can accurately identify and quantify the measurement signal deviation caused by the change of detector probe fluid dynamics characteristics and the difference of wine fluid resistance characteristics, improve the precision and accuracy of liquid density detection.

Description

Technical Field

[0001] This application relates to the field of liquid density detection technology, and in particular to a method, equipment and storage medium for detecting the density of flowing wine in a pipeline. Background Technology

[0002] During the production of alcoholic beverages, the density of the liquid flowing in the pipeline is measured in real time and accurately. However, in actual operation, there may be interference factors such as deposits on the surface of the density sensor probe due to long-term use, or the need to use non-standard calibrators due to special circumstances during the calibration process. These factors may cause deviations in the measurement results.

[0003] To ensure the accuracy of density sensor probe measurements, the system performs calibration operations periodically. By introducing a standard density liquid, the sensor is calibrated to generate a new set of calibration curve data, which is used to calculate the real-time density of the liquid in the pipeline.

[0004] In certain unforeseen circumstances, due to insufficient supply of upstream chemical raw materials, a batch of finished liquid products that have passed quality inspection and have clearly defined physicochemical properties may be used as alternative calibration solutions to avoid prolonged production line shutdowns and resulting economic losses. These solutions have known and accurate density values, but their fluid resistance is significantly lower than that of commonly used official standard calibration solutions.

[0005] Due to the combined effect of the low fluid resistance of the alternative liquid and the deposited layer on the probe surface, when calibrating and generating new calibration curve data, the probe's response characteristics to the low fluid resistance liquid may be inadvertently embedded into the curve. Therefore, while the calibration curve may be mathematically valid, it contains incorrect assumptions about the sensor's behavior under specific fluid resistance conditions at a physical level.

[0006] After calibration, if the system switches to processing a new batch of liquid with significantly higher fluid resistance than the previously used calibration fluid, the probe's physical response is simultaneously affected by both the liquid's true density and its high fluid resistance, resulting in superimposed signal changes. When this superimposed signal is processed using calibration curve data containing hidden defects, the system incorrectly interprets the signal change caused by high fluid resistance as a decrease in liquid density because the curve was generated with an incorrectly fixed response under low fluid resistance characteristics. Although the density value deviates but does not reach the warning threshold, the monitored density curve appears normal. However, the actual density measurement of the flowing liquid in the pipeline has deviated, resulting in decreased accuracy. This creates complex quality traceability problems for the production line and causes economic losses. Summary of the Invention

[0007] The main objective of this application is to provide a method, device, and storage medium for detecting the density of flowing wine in a pipeline, so as to solve one or more technical problems existing in the prior art, and at least provide a beneficial option or create conditions.

[0008] To achieve the above objectives, one aspect of this application proposes a method for detecting the density of flowing wine in a pipeline, the method comprising: The calibration operation is performed to obtain the vibration frequency and driving energy consumption value generated by the vibration density detector in the calibration liquid under different fluid resistance conditions, and a mapping dataset is formed based on the density of the calibration liquid. Based on the mapping dataset, a multi-dimensional calibration relationship is established; wherein, the driving energy consumption value represents the fluid resistance, and the multi-dimensional calibration relationship represents the mapping law between the vibration frequency and the true density under different fluid resistances; When the production line is in normal operation, the real-time vibration frequency and real-time drive energy consumption value generated by the vibration density detector are obtained; Based on the real-time drive energy consumption value and the multi-dimensional calibration relationship, the real-time vibration frequency is analyzed. When it is determined that the real-time vibration frequency contains a deviation caused by fluid resistance, fluid resistance compensation is performed on the real-time vibration frequency. Based on the compensated real-time vibration frequency, the real-time drive energy consumption value, and the multi-dimensional calibration relationship, the final density is determined. When the final density is within the set density range, the final density is used as the density of the liquid to be tested.

[0009] In some embodiments, the process of obtaining the drive energy consumption value includes: When the production line is in the preset reference state, it is confirmed that the reference flow medium in the pipeline is stable, the damping effect on the vibration density detector is constant, and the vibration density detector is driven to vibrate with the set constant amplitude. The instantaneous power required to maintain constant vibration of the vibration density detector is obtained, and the required instantaneous power is used as the energy baseline to periodically update the energy baseline. Obtain the real-time total drive energy consumption value currently output by the vibration density detector, and calculate the energy consumption difference between the real-time total drive energy consumption value and the energy baseline of the current production cycle; When the energy consumption difference is within the set energy range, the energy consumption difference is used as the drive energy consumption value.

[0010] In some embodiments, establishing a multi-dimensional calibration relationship based on the density of the calibration liquid and the mapping dataset includes: Based on the driving energy consumption values ​​of the calibration liquid under different fluid resistance conditions, the influence of fluid resistance on the vibration frequency is captured and quantified to obtain the quantitative relationship; Based on the quantization relationship, the density of the calibration liquid, and the mapping dataset, a multi-dimensional calibration relationship is established that includes the relationship between the density, the vibration frequency, and the driving energy consumption value.

[0011] In some embodiments, analyzing the real-time vibration frequency based on the real-time drive energy consumption value and the multi-dimensional calibration relationship includes: The real-time drive energy consumption value is used as the input parameter of the multi-dimensional calibration relationship to obtain the drive energy consumption value and the vibration frequency during calibration under the same fluid resistance conditions. Based on the real-time drive energy consumption value, the drive energy consumption value during calibration, and the vibration frequency during calibration, identify whether the real-time vibration frequency contains a deviation component caused by fluid resistance.

[0012] In some embodiments, determining the final density based on the compensated real-time vibration frequency, the real-time drive energy consumption value, and the multi-dimensional calibration relationship includes: The real-time vibration frequency and the real-time driving energy consumption value are input into the multi-dimensional calibration relationship to calculate the real-time density; Based on the compensated real-time vibration frequency and the multi-dimensional calibration relationship, the real-time density is corrected to obtain the final density.

[0013] In some embodiments, the method further includes: During the calibration operation, the temperature of the calibration liquid is adjusted according to multiple discontinuous temperature points to generate different fluid resistance conditions; A calibration dataset is formed based on the density of the calibration liquid, the corresponding temperature points under different fluid resistance conditions, the corresponding vibration frequency, and the corresponding drive energy consumption value. In the calibration dataset, when there are at least two sets of data with the same corresponding drive energy consumption value and the rest of the data are different, the at least two sets of data are regarded as conflict data sets, all the temperature points in the conflict data sets are extracted, and the temperature inflection point is calculated. Using the temperature inflection point as the boundary, the calibration dataset is divided into a high-temperature mapping dataset and a low-temperature mapping dataset. Based on the high-temperature mapping dataset and the low-temperature mapping dataset, the multi-dimensional calibration relationship for high temperature and the multi-dimensional calibration relationship for low temperature are established in the partition. Based on the real-time temperature of the liquid to be tested and the temperature inflection point, the corresponding multi-dimensional calibration relationship for high temperature or low temperature is invoked.

[0014] In some embodiments, the method further includes: Monitor the real-time driving energy consumption value and, based on the trend, confirm whether the real-time driving energy consumption value continuously deviates from the set expected range. When the real-time drive energy consumption value continuously deviates from the set expected energy range, an auxiliary warning signal is generated and issued.

[0015] In some embodiments, the method further includes: Monitor the long-term trend of the energy baseline to confirm whether the energy baseline continues to deviate from the historical normal range or shows an upward trend; When the energy baseline continuously deviates from the historical normal range of the set baseline or shows an upward trend, a warning signal for detector performance degradation is generated and issued.

[0016] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0017] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0018] The embodiments of this application include at least the following beneficial effects: This application provides a method, device, and storage medium for detecting the density of flowing wine in a pipeline. This scheme synchronously collects the vibration frequency and driving energy consumption value of a vibrating density detector to form a mapping dataset. The driving energy consumption value required by the vibrating density detector to maintain constant amplitude vibration is used as an auxiliary information source that can reflect the changes in liquid fluid resistance in real time. During the calibration stage, a multi-dimensional mapping relationship between the two and the true density is established, realizing the accurate quantification and compensation of the interference of fluid resistance on the density measurement signal. When there is a micro-deposit layer on the surface of the detector probe, and the calibration process uses a material with significantly different fluid resistance characteristics from the wine being tested, this method is effective. When using the same alternative liquid, the system accurately identifies and quantifies the measurement signal deviation caused by the changes in the hydrodynamic characteristics of the detector probe and the differences in the fluid resistance characteristics of the liquid through multi-dimensional mapping relationships and real-time monitoring of drive energy consumption. It also distinguishes the contribution of the deviation from the actual density change, thereby preventing the system from erroneously interpreting the signal change caused by the fluid resistance effect as a decrease in density, thus improving the accuracy and precision of liquid density detection. Without adding extra hardware or changing the production process, the system effectively solves the problem that traditional density measurement systems cannot accurately identify and compensate for signal deviations caused by non-density factors when there is a micro-deposit layer on the surface of the detector probe and when non-standard alternative liquids are used in the calibration process. Attached Figure Description

[0019] Figure 1 This is a flowchart of a method for detecting the density of flowing wine in a pipeline according to an embodiment of this application; Figure 2 This is a flowchart of a method for detecting the density of flowing wine in a pipeline according to another embodiment of this application; Figure 3 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0021] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0022] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0023] Unless otherwise defined, 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 application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0024] As described in the background art, in the prior art, due to insufficient supply of upstream chemical raw materials, in order to avoid long-term shutdown of the production line and resulting economic losses, a batch of finished liquid products that have passed quality inspection and have clear physicochemical indicators are used as substitute calibrators to maintain the continuous operation of the production line.

[0025] However, this batch of ready-made liquid, used as a temporary substitute, while having a known and accurate density, exhibited significantly lower fluid resistance than the commonly used official standard calibration solution. This difference in physical properties, though considered controllable during the decision-making process, foreshadowed subsequent problems. Meanwhile, during prolonged operation, a microscopic deposit of tartrates formed on the surface of the density detector probe that came into direct contact with the liquid. This deposit altered the hydrodynamic properties of the probe surface, causing its physical response to interact with the liquid to no longer match its initial state.

[0026] During the calibration process, the detector was adjusted based on the known density of the substitute liquid, generating new calibration curve data. This process appears to correctly correct the initial density measurement point. However, due to the combined effect of the low fluid resistance of the substitute liquid and the deposited layer on the probe surface, the system inadvertently embedded the probe's response characteristics to the low fluid resistance liquid into the generated calibration curve. This means that while the calibration curve is mathematically valid, it contains incorrect assumptions about the detector's behavior under specific fluid resistance conditions at a physical level.

[0027] After calibration, the production line switched to processing a substitute finished liquid with much higher fluid resistance than the one previously used for calibration. When this high-resistance liquid flows through the probe, the probe's physical response is simultaneously affected by both the liquid's true density and its high fluid resistance, resulting in superimposed signal changes.

[0028] The system then used calibration curve data containing hidden defects to process the superimposed signal. Because the curve incorrectly fixed the response under low fluid resistance characteristics during generation, when it encountered signal changes caused by high fluid resistance, the system incorrectly interpreted this part of the signal change caused by high fluid resistance as a decrease in liquid density.

[0029] Although the calculated density values ​​had some deviation, they exhibited stability, and the deviation did not reach the preset threshold for triggering a single-point over-limit alarm. Therefore, the density curve on the monitoring interface showed a smooth "qualified" trend, without any obvious abnormal indications. Based on the "qualified" data displayed on the monitoring interface, coupled with the reduced frequency of manual verification, the error within the system went undetected for a long time. It was only during manual sampling verification that the density of the entire batch of liquid was found to be non-compliant. By this time, the non-compliant product had already been filled, causing irreparable economic losses and creating complex quality traceability challenges.

[0030] This application aims to provide a method for detecting the density of flowing liquid in a pipeline. During the density measurement of flowing liquid in a pipeline, when there is a micro-deposit layer on the surface of the detector probe and the calibration process uses a substitute liquid with significantly different fluid resistance characteristics from the liquid being measured, the method can accurately identify and quantify the measurement signal deviation caused by the change in the fluid dynamic characteristics of the detector probe and the difference in the fluid resistance characteristics of the liquid, and output the accurate liquid density of the liquid being measured, thereby improving the precision and accuracy of density detection.

[0031] Figure 1 This is an optional flowchart of a method for detecting the density of flowing wine in a pipeline provided in this application embodiment. This method is mainly applied to liquid production lines equipped with vibration density detectors, such as wine production lines. Figure 1 The method may include, but is not limited to, steps S100 to S500.

[0032] Step S100: Perform calibration operation to obtain the vibration frequency and driving energy consumption value generated by the vibration density detector of the calibration liquid under different fluid resistance conditions, and form a mapping dataset based on the density of the calibration liquid.

[0033] Step S200: Establish multi-dimensional calibration relationships based on the mapping dataset.

[0034] Step S300: When the production line is in normal operation, acquire the real-time vibration frequency and real-time drive energy consumption value of the liquid to be tested by the vibration density detector.

[0035] Step S400: Analyze the real-time vibration frequency based on the real-time drive energy consumption value and the multi-dimensional calibration relationship. When it is determined that the real-time vibration frequency contains a deviation caused by fluid resistance, fluid resistance compensation is performed on the real-time vibration frequency.

[0036] Step S500: Determine the final density based on the compensated real-time vibration frequency, real-time drive energy consumption value and multi-dimensional calibration relationship. When the final density is within the set density range, the final density is used as the density of the liquid to be tested.

[0037] Steps S100 to S500 as illustrated in this embodiment of the application synchronously acquire the vibration frequency and drive energy consumption value of the vibrating density detector to form a mapping dataset. The drive energy consumption value required by the vibrating density detector to maintain constant amplitude vibration is used as an auxiliary information source that can reflect the changes in liquid fluid resistance in real time. During the calibration stage, a multi-dimensional mapping relationship between the two and the true density is established, realizing the accurate quantification and compensation of the interference of fluid resistance on the density measurement signal. When there is a micro-deposit layer on the detector probe surface, and the calibration process uses a substitute liquid with significantly different fluid resistance characteristics from the liquid being tested, the multi-dimensional mapping relationship and the real-time drive energy consumption value are used to accurately quantify and compensate for the interference of fluid resistance on the density measurement signal. The system accurately identifies and quantifies the measurement signal deviation caused by the combined effects of changes in the hydrodynamic characteristics of the detector probe and the differences in the fluid resistance characteristics of the wine, and distinguishes its contribution from the actual density change. This prevents the system from incorrectly interpreting signal changes caused by fluid resistance as a decrease in density, and prevents the conventional over-limit alarm mechanism from failing to provide timely warnings when the measurement results are stable but have persistent deviations. Without adding extra hardware or changing the production process, the system effectively solves the problem that traditional density measurement systems cannot accurately identify and compensate for signal deviations caused by non-density factors when there is a micro-deposit layer on the detector probe surface and non-standard substitute liquids are used in the calibration process.

[0038] In some embodiments of S100, the liquid production line is detected to determine if a calibration operation is being performed. During the calibration operation, regardless of whether a standard calibration liquid or an alternative calibration liquid is used due to supply chain disruption, the known precise density value of the calibration liquid is recorded.

[0039] The parameters of fluid resistance conditions include pump speed and liquid temperature. Specifically, different fluid resistance conditions are created by adjusting the pump speed of the liquid pump and / or regulating the liquid temperature within the pipeline. For example, calibration is performed at 20°C and 30°C using alternative calibration liquids, or at 80% and 100% of the rated flow rate. Other parameters of the fluid resistance conditions may include input flow rate or other parameters, which will not be detailed in this embodiment.

[0040] Under different fluid resistance conditions, the vibration density detector detects the calibration liquid and simultaneously records the vibration frequency and driving energy consumption generated by the detection.

[0041] In this embodiment, the vibration density detector can be a U-tube density meter, whose internal U-shaped measuring tube is excited by a piezoelectric ceramic actuator. By exciting the vibration density detector, it vibrates at a specific resonant frequency and constant amplitude. The vibration density detector can also be a single-straight-tube density meter or other vibration detectors, which will not be described in detail in this embodiment.

[0042] The vibration frequency is the core vibration frequency signal output by the vibration density detector. This signal directly reflects the effective mass of the U-tube in the current liquid, and its main variation is related to the liquid density. The vibration frequency of the U-tube can be measured in real time using a high-precision frequency counter.

[0043] The driving energy consumption value is the instantaneous electrical power required for the vibrating density detector to continuously vibrate at a preset constant amplitude. This electrical power value directly reflects the damping effect experienced by the probe in the current liquid, that is, it reflects the fluid resistance of the liquid to the vibration of the vibrating density detector. The driving energy consumption value can characterize the fluid resistance. Specifically, the driving current and driving voltage of the vibrating density detector can be obtained through current detectors and voltage detectors, and the required instantaneous electrical power can be calculated using the driving current and driving voltage.

[0044] For example, when a high-sweetness liquid flows through, or when a micro-deposit layer forms on the probe surface due to long-term use, causing changes in local hydrodynamic characteristics and increasing the fluid resistance of the liquid flowing in the pipe, the driving energy required to maintain the same amplitude will also increase accordingly. That is, the driving energy consumption is proportional to the fluid resistance.

[0045] Through fluid resistance conditions Vibration frequency corresponding to the current fluid resistance conditions and the driving energy consumption value corresponding to the current fluid resistance conditions This forms a data set, and multiple data sets are obtained under different fluid resistance conditions. , , Based on the known density of the calibration liquid This forms a mapping dataset ( , , , This provides the conditions for creating the mapping relationship between fluid resistance and vibration frequency, as well as the mapping relationship between vibration frequency and true density.

[0046] Based on the S100 embodiment, when measuring liquid density, the core vibration frequency signal of the vibratory density detector is affected not only by the liquid mass (i.e., density) but also by the damping effect of liquid fluid resistance (e.g., viscosity) on the detector vibration. By introducing synchronous monitoring of the detector's drive energy consumption, the power consumption required for the detector to overcome fluid resistance in the liquid is directly reflected, and the drive energy consumption becomes an auxiliary signal that can reflect changes in fluid resistance in real time. Compared with the prior art that only focuses on the single correspondence between the core vibration frequency signal and density, this application incorporates the important factor of fluid resistance into density detection. When there is a micro-deposit layer on the detector probe surface, or when there is a significant difference in the fluid resistance characteristics between the calibration liquid and the liquid to be tested, the accuracy and precision of liquid density detection are improved by mapping the fluid resistance to vibration frequency and the fluid density to vibration frequency, avoiding continuous deviations in the measurement results.

[0047] In some embodiments of S200, by calibrating the driving energy consumption value of the liquid under different fluid resistance conditions, the influence of fluid resistance on vibration frequency is accurately captured and quantified, and the quantitative relationship between fluid resistance and vibration frequency is obtained, that is, the quantitative relationship between driving energy consumption value and vibration frequency.

[0048] For example, when the calibration liquid has low fluid resistance, the drive energy consumption is also low. In this case, the vibration frequency and the known density of the calibration liquid are recorded and used together with the data under high fluid resistance conditions to establish a relationship. This allows subsequent calibration relationships to "understand" how changes in fluid resistance affect the core vibration frequency signal, thereby avoiding the erroneous embedding of the low fluid resistance characteristics of the substitute liquid into the calibration curve.

[0049] Based on multiple sets of data in the S100 mapping dataset and the known density of the corresponding calibration liquid, and taking the quantization relationship as one of the dimensions of the calibration relationship, a multi-dimensional calibration relationship is determined, which includes the relationship between density, core vibration frequency signal and driving energy consumption value.

[0050] In this application, the multi-dimensional calibration relationship can characterize the mapping between vibration frequency and true density under different fluid resistance effects. Based on this multi-dimensional calibration relationship, it can be deduced that the vibration frequency can be analyzed and calibrated by using the driving energy consumption value characterizing fluid resistance, and the liquid density of the corresponding liquid in the pipeline can be accurately determined by using the driving energy consumption value and the vibration frequency. In other words, the multi-dimensional calibration relationship can take into account the signal deviation caused by the difference in fluid resistance characteristics between the substitute calibration liquid and the production liquid to be tested, as well as the change in fluid dynamic characteristics caused by the micro-deposition layer on the probe surface, and thus correct it in subsequent measurements.

[0051] Based on the S200 implementation, by synchronously acquiring the vibration frequency and drive energy consumption value, a multi-dimensional mapping relationship between the two and the true density is established during the calibration phase. This achieves accurate quantification and compensation for the interference of fluid resistance on the density measurement signal. Without adding extra hardware or changing the production process, it effectively solves the problem that traditional density measurement systems cannot accurately identify and compensate for signal deviations caused by non-density factors when there is a micro-deposit layer on the detector probe surface and non-standard substitute liquids are used in the calibration process.

[0052] In some embodiments of S300, the liquid production line is detected, operating parameters are obtained, and the current state of the liquid production line is determined. When it is determined that the current production line is in normal operation, the liquid flowing in the pipeline is the liquid to be tested, and the density of the liquid to be tested is unknown.

[0053] Under the current fluid resistance conditions, the vibration density detector detects the liquid under test and simultaneously records the real-time vibration frequency and real-time driving energy consumption value generated by the detection.

[0054] Similarly, the vibration frequency at this point reflects the effective mass of the U-tube of the vibrating density detector in the liquid, and its main variation is related to the liquid density. The driving energy consumption value at this point reflects the damping effect experienced by the probe in the current liquid, that is, it reflects the fluid resistance of the liquid to the vibration of the vibrating density detector. The driving energy consumption value can characterize the fluid resistance.

[0055] In some embodiments of S400, the current production line is in normal operation. During the real-time measurement phase, the real-time drive energy consumption value is used as an input parameter of the multi-dimensional calibration relationship to obtain the drive energy consumption value and vibration frequency under the same fluid resistance condition (i.e., the current fluid resistance condition in S300) as the real-time drive energy consumption value, i.e., the drive energy consumption value and vibration frequency at the time of calibration.

[0056] By comparing the real-time drive energy consumption value with the drive energy consumption value during calibration, and based on the vibration frequency during calibration and the multi-dimensional calibration relationship, the real-time vibration frequency is analyzed to identify whether there are any deviation components in the real-time vibration frequency caused by changes in liquid fluid resistance.

[0057] If so, multi-dimensional calibration relationships are used to compensate for the deviation in vibration frequency caused by changes in liquid fluid resistance in real time.

[0058] If not, the multi-dimensional calibration relationship established during the calibration phase is used to calculate the real-time density of the liquid flowing through the pipe, taking the real-time vibration frequency and real-time drive energy consumption as inputs, and this real-time density is used as the final density.

[0059] For example, when a high-sweetness liquid flows through the probe, its higher fluid resistance will cause the drive energy consumption value to increase, and may also cause the vibration frequency to decrease slightly. Based on the increased drive energy consumption value, the multi-dimensional calibration relationship is used to identify that the decrease in the core vibration frequency signal is caused by fluid resistance rather than a decrease in actual density, and corresponding corrections are made to output an accurate density value.

[0060] Based on the S400 implementation, through multi-dimensional calibration relationships, the influence of fluid resistance on the core vibration frequency signal can be accurately quantified and compensated. This allows for accurate resolution of the true density value, avoiding misinterpretation of fluid resistance effects, improving the reliability of the vibration frequency, and ensuring its value as a mapping of liquid density, free from interference factors. It precisely identifies and quantifies the measurement signal deviation caused by changes in the detector probe's hydrodynamic characteristics and differences in the liquid's fluid resistance characteristics, distinguishing its contribution to the actual density change, thereby facilitating the subsequent output of more accurate liquid density values.

[0061] In some embodiments of S500, the final density after fluid resistance compensation is calculated based on the multi-dimensional relationship established during the calibration phase, according to the compensated real-time vibration frequency and real-time drive energy consumption value.

[0062] The final density obtained after compensation calculation is compared with the set density range.

[0063] When the final density is within the set density range, the final density is taken as the liquid density of the liquid to be tested.

[0064] When the final density is not within the set density range, a density anomaly alarm is generated and displayed through the human-interaction interface. A shutdown command or diversion command is generated and sent to the production line control system to control the current liquid production to perform shutdown or diversion processing.

[0065] The density range can be set based on historical density data of the liquid being tested, or it can be set according to user needs. In this application, no specific limitation is made on the density range. For example, the density of the wine should be between 1.02 g / cm³ and 1.06 g / cm³, while the final calculated density of the liquid being tested is 1.01 g / cm³.

[0066] Based on the S500 implementation, when the drive energy consumption value indicates that the current fluid resistance is high, even if the core vibration frequency signal is reduced, the main processing unit can determine that this reduction is due to fluid resistance rather than a decrease in actual density based on the calibration relationship. This allows for correction when calculating the final density, avoiding the erroneous interpretation of fluid resistance effect as a decrease in density. It provides an early warning scheme for production line collaborative control after exceeding the set density range, reducing economic losses.

[0067] Based on embodiments S100 to S500, by simultaneously acquiring the core vibration frequency signal and driving energy consumption value, and establishing a multi-dimensional relationship between these two and the true density during the calibration phase, the influence of fluid resistance on the core vibration frequency signal is accurately quantified and compensated. This allows for accurate resolution of the true density value even when a substitute liquid with significantly different fluid resistance characteristics from the tested liquid is used during calibration, avoiding the erroneous interpretation of fluid resistance effects as density reduction. This method, without adding any external hardware or altering the production process, achieves strong immunity to hidden interference sources solely through in-depth interpretation of existing detector internal information, significantly reducing product quality risks and economic losses caused by measurement errors.

[0068] This application shifts the perspective in the field of density detection from merely focusing on the detector's output to delving into the information inherent in its operation. It recognizes that the "driving energy consumption" for maintaining a vibratory detector at a constant amplitude is not simply an operating cost, but a physical quantity directly reflecting the magnitude of fluid resistance experienced by the probe in the liquid. This decision elevates what was originally considered an internal operating parameter to a second source of information as important as the density signal.

[0069] In some embodiments of the present invention, the process of obtaining the drive energy consumption value may include, but is not limited to, the following specific steps: S110, when the production line is in the preset reference state, it is confirmed that the reference flow medium in the pipeline is stable, the damping effect on the vibrating density detector is constant, and the vibrating density detector is driven to vibrate with the set constant amplitude.

[0070] S120: Obtain the instantaneous power required to maintain constant vibration of the current vibration density detector, use the required instantaneous power as the energy baseline, and periodically update the energy baseline.

[0071] S130: Obtain the real-time total drive energy consumption value currently output by the vibration density detector, and calculate the energy consumption difference between the real-time total drive energy consumption value and the energy baseline of the current production cycle.

[0072] S140, when the energy consumption difference is within the set energy range, the energy consumption difference is used as the drive energy consumption value.

[0073] S150 monitors the long-term trend of energy baseline changes to confirm whether the energy baseline continues to deviate from the historical normal range or shows an upward trend.

[0074] S151 When the energy baseline continuously deviates from the historical normal range of the set baseline or shows an upward trend, a warning signal for detector performance degradation is generated and issued.

[0075] In a wine production line, when the piezoelectric ceramic actuator inside a vibratory density detector degrades due to long-term operation, causing the driving energy consumption required to maintain constant vibration to continuously increase, and this, combined with changes in the actual fluid resistance of the external flowing wine, results in an additive anomaly in the total driving energy consumption, it is necessary to accurately distinguish and quantify the contributions of the performance degradation of the internal components of the detector and the changes in the external wine fluid characteristics to the total energy consumption. This will provide an accurate diagnosis of the detector's own health status and avoid the misjudgment of the root cause of the fault by operators due to the existing general fluid resistance anomaly warning.

[0076] In some embodiments of S110, the liquid production line is detected, operating parameters are acquired, and the current state of the liquid production line is determined. When it is determined that the current production line is in a preset reference state, an internal energy baseline calibration procedure is triggered to perform an energy baseline calibration operation. At this time, the reference flow medium in the pipeline is stable, and the reference flow medium has known and stable fluid resistance characteristics, and its damping effect on the vibration of the vibration density detector is negligible or constant.

[0077] In this embodiment, when the liquid production line is in a preset reference state, the pipeline is filled with a standard cleaning solution (such as pure water) or a reference flow medium that is in a vented state (filled with air). The reference flow medium in the pipeline in the preset reference state can also be other stable and known reference media, and this embodiment does not impose specific limitations.

[0078] Based on the preset reference state, an energy baseline calibration operation is performed, and the vibration density detector is controlled to vibrate at a set constant amplitude.

[0079] The constant amplitude can be set according to actual needs, and no specific restrictions are imposed in this application.

[0080] In some embodiments of S120, the instantaneous electrical power required to maintain constant vibration of the current vibration density detector is obtained, i.e., the instantaneous power.

[0081] Based on the preset reference state in S110, the fluid medium inside the pipe is known and stable, and its fluid resistance has a controllable or known impact on the total energy consumption. Therefore, the measured instantaneous electrical power can accurately reflect the true working state and efficiency of the excitation components inside the detector. The instantaneous power measured in the current cycle is taken as the energy baseline within the current production cycle, which is entirely determined by the current performance state of the internal components of the density detector.

[0082] An energy baseline is stored and associated with a measurement timestamp. This baseline serves as a reference value for compensating for performance degradation of internal components in subsequent real-time measurements during the current production cycle (S300). The liquid production line periodically repeats S110 and S120. This energy baseline is automatically updated after each cleaning or batch change, enabling periodic updates to accommodate the slow performance degradation of the density detector's internal components over time.

[0083] In some embodiments of S130, the vibration density detector performs a detection operation on the liquid in the pipe, obtains the real-time total drive energy consumption value currently output by the vibration density detector, and subtracts the latest energy baseline from the real-time total drive energy consumption value to obtain the energy consumption difference.

[0084] The latest energy baseline is the energy baseline stored in the current production cycle.

[0085] The energy consumption difference is the energy consumption caused purely by the actual fluid resistance characteristics of the liquid flowing in the pipe, and can be called net fluid resistance energy.

[0086] Based on the S130 embodiment, even if the internal actuator efficiency of the density detector decreases, leading to an increase in its basic energy consumption, this internal loss will be accurately deducted when calculating the net fluid resistance energy, thereby effectively separating the respective contributions of internal component performance degradation and external fluid characteristic changes to the total energy consumption.

[0087] In some embodiments of S140, the calculated energy consumption difference is compared with the set energy range to determine whether the energy consumption difference is within the set energy range.

[0088] If so, the energy consumption difference is used as the drive energy consumption value. This drive energy consumption value is the drive energy consumption value generated by the vibration density detector during calibration and normal operation. In other words, the drive energy consumption value generated by the vibration density detector requires the execution of operation S130.

[0089] If not, an abnormal fluid resistance warning will be generated, indicating to the operator that the viscosity and other properties of the current liquid may be deviated.

[0090] The energy range can be set according to actual needs, and the specific parameters of the energy range are not specifically limited in this application.

[0091] In some embodiments of S150 to S151, the long-term trend of the energy baseline is independently monitored and tracked. Based on this long-term trend, it is determined whether the energy baseline continues to deviate from the historical normal range, or whether the energy baseline shows an upward trend.

[0092] A sustained deviation from the baseline's historical normal range refers to the energy baseline not falling within the baseline's set historical normal range within a specified time window. The set time window can be configured to a duration as needed, and this duration is not specifically limited in this application. The set historical normal range can be obtained by filtering, interpolating, and sorting the historical data using the energy baseline stored in S120 as historical data. This application does not impose specific limitations on the baseline's historical normal range parameter.

[0093] An upward trend means that within the set time window, the energy baseline of the previous production cycle is lower than the energy baseline of the current production cycle. The duration of the set time window can be set according to demand, and this duration is not specifically limited in this application.

[0094] When the energy baseline continues to deviate from the normal historical range or the energy baseline shows an upward trend, even if the energy baseline is within the energy range set in S140, a detector performance degradation warning signal will be generated independently and issued.

[0095] The detector performance degradation warning signal aims to clearly inform staff that the internal actuators of the detector may be experiencing efficiency degradation, requiring inspection or maintenance. Through the dual warning mechanism from S140 to S151, the contributions of internal component performance degradation and changes in external fluid characteristics to the total energy consumption can be accurately distinguished and quantified, thereby providing staff with a precise diagnosis of the root cause of the fault and avoiding misdiagnosis.

[0096] Through the embodiments in S110 to S151, the inherent periodic preset reference states of the liquid production line are used to calibrate and compensate for the performance degradation of the internal components of the detector. During the cleaning or batch changeover intervals of the liquid production line, the pipeline is filled with reference flow media. These reference flow media share the common characteristic that their fluid resistance characteristics are known and stable, and their damping effect on the vibration of the vibrating density detector is constant or negligible. When the preset reference state is detected, an internal energy baseline calibration procedure is automatically executed, measuring the electrical power required to maintain constant vibration of the vibrating density detector. Since the influence of external fluid resistance is effectively controlled at this time, the measured electrical power value accurately reflects the true operating state and efficiency of the internal excitation components of the detector. This value serves as the energy baseline, which is no longer a fixed initial value but can be periodically updated as the internal components of the detector slowly age.

[0097] During the subsequent wine production process, the real-time total drive energy consumption is continuously monitored. This real-time total energy value is then subtracted from the most recently measured energy baseline, i.e., the energy baseline stored in the current production cycle. The resulting difference purely represents the energy consumption caused by the actual fluid resistance of the wine flowing in the pipe, and can be called net fluid resistance energy. This ensures that even if the efficiency of the internal actuator of the detector decreases, leading to an increase in its base energy consumption, this internal loss is accurately deducted when calculating the net fluid resistance energy, thus effectively separating the respective contributions of internal component performance degradation and external fluid characteristic changes to the total energy consumption.

[0098] This separation capability enables this application to provide more accurate early warning information. It can not only determine whether the fluid resistance of the liquid is abnormal based on whether the net fluid resistance energy exceeds the range, but also independently track the changing trend of the energy baseline. If the energy baseline continuously and slowly increases, even if the net fluid resistance energy of the liquid is within the normal range, an early warning of performance degradation of the internal components of the detector will be issued. This contrasts with existing technologies, which, when facing performance degradation of internal components, cannot distinguish between internal losses and external fluid resistance, and may continuously issue alarms for abnormal fluid resistance even if the external liquid characteristics are normal, thus misleading operators in their troubleshooting. This embodiment, by providing two independent and clear diagnostic dimensions, allows personnel to directly understand the true root cause of the problem, determining whether it is due to the aging of the detector itself or a genuine change in the liquid characteristics, thereby avoiding misjudgment of the root cause of the fault and enabling timely and targeted maintenance or adjustment measures. Furthermore, it has a wide range of applications, suitable for industrial scenarios where the performance of internal components of the detector may drift during long-term operation, and where the production process has periodic reference states available.

[0099] In some embodiments of the present invention, the process of establishing the multi-dimensional calibration relationship in S200 may include, but is not limited to, the following specific steps: S210: Based on the driving energy consumption value of the calibration liquid under different fluid resistance conditions, the influence of fluid resistance on vibration frequency is captured and quantified to obtain the quantitative relationship.

[0100] S220 establishes a multi-dimensional calibration relationship based on quantization relationships, the density of calibration liquids, and mapping datasets, encompassing the interrelationships between density, vibration frequency, and driving energy consumption values.

[0101] In some embodiments of S210, by calibrating the driving energy consumption value of the liquid under different fluid resistance conditions, the influence of fluid resistance on vibration frequency is accurately captured and quantified, and the quantitative relationship between fluid resistance and vibration frequency is obtained, that is, the quantitative relationship between driving energy consumption value and vibration frequency.

[0102] For example, when the calibration liquid has low fluid resistance, the drive energy consumption is also low. In this case, the vibration frequency and the known density of the calibration liquid are recorded and used together with the data under high fluid resistance conditions to establish a relationship.

[0103] This allows subsequent calibration relationships to "understand" how changes in fluid resistance affect the core vibration frequency signal, thus avoiding the erroneous embedding of the low fluid resistance characteristics of the substitute liquid into the calibration curve. It also enables the quantification of the interference of fluid resistance on the density measurement signal.

[0104] In some embodiments of S220, based on multiple sets of data in the mapping dataset and the known density of the corresponding calibration liquid, and taking the quantization relationship as one of the dimensions of the calibration relationship, a multi-dimensional calibration relationship is determined, which includes the relationship between density, core vibration frequency signal and driving energy consumption value.

[0105] In this embodiment, a multidimensional calibration relationship can be generated through multivariate regression analysis or by constructing a two-dimensional lookup table.

[0106] For example, this relationship can be expressed as: density = (Vibration frequency, driving energy consumption value), where... It can be a function or lookup table determined by calibration data.

[0107] In this application, the multi-dimensional calibration relationship can characterize the mapping between vibration frequency and true density under different fluid resistance effects. Based on this multi-dimensional calibration relationship, it can be deduced that the vibration frequency can be analyzed and calibrated by using the driving energy consumption value characterizing fluid resistance, and the liquid density of the corresponding liquid in the pipeline can be accurately determined by using the driving energy consumption value and the vibration frequency. In other words, the multi-dimensional calibration relationship can take into account the signal deviation caused by the difference in fluid resistance characteristics between the substitute calibration liquid and the production liquid to be tested, as well as the change in fluid dynamic characteristics caused by the micro-deposition layer on the probe surface, and thus correct it in subsequent measurements.

[0108] In another embodiment, a density measurement model for fluid resistance changes is constructed based on multiple data sets in the mapping dataset and the known densities of the corresponding calibration liquids. This model can output the relationship between density, core vibration frequency signal, and driving energy consumption value. Using vibration frequency and driving energy consumption value as input parameters, it can output liquid density while also analyzing and correcting the vibration frequency. The density measurement model can be constructed using other mapping models or neural network models, which will not be detailed in this embodiment.

[0109] Through the embodiments of S210 to S220, the vibration frequency and driving energy consumption value synchronously acquired in S100 are used to establish a multi-dimensional mapping relationship between the two and the true density or a density measurement model for fluid resistance changes during the calibration stage. This achieves accurate quantification and compensation for the interference of fluid resistance on the density measurement signal. Without adding extra hardware or changing the production process, it effectively solves the problem that traditional density measurement systems cannot accurately identify and compensate for signal deviations caused by non-density factors when there is a micro-deposit layer on the detector probe surface and non-standard substitute liquids are used in the calibration process.

[0110] In some embodiments of the present invention, the real-time vibration frequency analysis process in S400 may include, but is not limited to, the following specific steps: S410 uses the real-time drive energy consumption value as the input parameter for the multi-dimensional calibration relationship to obtain the drive energy consumption value and vibration frequency during calibration under the same fluid resistance conditions.

[0111] S420 identifies whether the real-time vibration frequency contains a deviation component caused by fluid resistance, based on the real-time drive energy consumption value, the drive energy consumption value during calibration, and the vibration frequency during calibration.

[0112] In some embodiments of S410, the current production line is in normal operation. During the real-time measurement phase, the real-time drive energy consumption value is used as an input parameter of the multi-dimensional calibration relationship, which can compensate for the deviation of the vibration frequency caused by the change of liquid fluid resistance in real time.

[0113] Changes in liquid fluid resistance can be caused by the inherent properties of the liquid itself, such as the viscosity of highly sweet liquids, or by deposits on the probe surface.

[0114] When the drive energy consumption value indicates that the current fluid resistance is high, even if the vibration frequency is reduced, it can be determined from the calibration relationship that this reduction is due to fluid resistance rather than a decrease in actual density. Therefore, a correction is made when calculating the final density to avoid incorrectly interpreting the fluid resistance effect as a decrease in density.

[0115] Obtain the current fluid resistance conditions corresponding to the real-time drive energy consumption value. Based on the current fluid resistance conditions and the mapping dataset, determine the corresponding calibration drive energy consumption value and calibration vibration frequency in the mapping dataset under the same fluid resistance conditions. Using the calibration data under the same fluid resistance conditions as a benchmark, compare the real-time drive energy consumption value with the calibration drive energy consumption value. Based on the calibration vibration frequency and multi-dimensional calibration relationships, identify whether there are deviation components in the real-time vibration frequency and analyze the causes of these deviation components.

[0116] In another embodiment, the fluid resistance conditions and vibration frequency under the same drive energy consumption value can be determined based on the real-time drive energy consumption value and the mapping dataset. By using the corresponding calibration data in the mapping dataset under the same fluid resistance conditions and / or the corresponding calibration data in the mapping dataset under the same drive energy consumption value—that is, by using the drive energy consumption value and vibration frequency at calibration with the same fluid resistance conditions as the current one, and the fluid resistance conditions and vibration frequency at calibration with the same real-time drive energy consumption value—the above data is used as a benchmark for comparative analysis. Multi-parameter comparison is then performed to identify whether there are any deviation components in the real-time vibration frequency and to analyze the causes of these deviation components.

[0117] In some embodiments of S420, the real-time drive energy consumption value, the drive energy consumption value during calibration as a reference, and the vibration frequency during calibration as a reference are compared. Based on the vibration frequency during calibration and the multi-dimensional calibration relationship, it is determined whether there is a change in the real-time vibration frequency, whether there is a deviation component in the real-time vibration frequency, and the cause of the deviation component is analyzed.

[0118] For example, a certain calibration wine, in the calibration operation, has the following mapping dataset: at 20°C, the driving energy consumption of the substitute wine is 55mW, and the core vibration frequency is X Hz; at 30°C (with slight changes in fluid resistance), the driving energy consumption is 52mW, and the core vibration frequency is Y Hz. Switching to a certain test wine, the real-time driving energy consumption of 85mW and the real-time vibration frequency of Z Hz are detected. Under the same fluid resistance conditions, the real-time driving energy consumption of 85mW is significantly higher than the driving energy consumption of 55mW for the calibration wine during calibration. The presence of a deviation component in the real-time vibration frequency Z Hz is identified, and the causes of this deviation are analyzed.

[0119] Based on multi-dimensional calibration relationships and calibration data, and according to real-time drive energy consumption values, the deviation component caused by fluid resistance in the real-time vibration frequency is identified. The real-time vibration frequency is then compensated and corrected according to the calibration relationships, thereby calculating the accurate real-time density value, i.e., the final density.

[0120] For example, the real-time drive energy consumption value is used as the input parameter of the multi-dimensional calibration relationship. Based on the real-time drive energy consumption value of 85mW, the multi-dimensional calibration relationship identifies that the real-time vibration frequency of Z Hz contains a deviation component caused by high fluid resistance. According to the multi-dimensional calibration relationship, Z Hz is compensated and corrected to calculate the accurate real-time density value, that is, to obtain the final density.

[0121] In another embodiment, the quantization relationship of one dimension in the multi-dimensional calibration relationship of S220 can be used to perform fluid resistance compensation on the real-time vibration frequency based on the change in the driving energy consumption value, thereby obtaining the compensated real-time vibration frequency. Alternatively, the vibration frequency obtained during calibration under the same fluid resistance conditions in S410 can be used as the compensated real-time vibration frequency, thus achieving fluid resistance compensation.

[0122] Through the embodiments of S410 to S420, by using real-time detection data as input parameters and substituting it into the multi-dimensional calibration relationship, the reference data for subsequent analysis and calibration is determined, which facilitates the identification of the deviation components of the vibration frequency, the analysis of the causes of the differential components of the vibration frequency, so as to compensate for the influence of fluid resistance on the vibration frequency, and effectively separate and quantify the interference of fluid resistance on the measurement signal.

[0123] In some embodiments of the present invention, the process of determining the final density in S500 may include, but is not limited to, the following specific steps: S510 inputs the real-time vibration frequency and real-time drive energy consumption value into the multi-dimensional calibration relationship to calculate the real-time density.

[0124] S520 corrects the real-time density based on the compensated real-time vibration frequency and multi-dimensional calibration relationship to obtain the final density.

[0125] In some embodiments of S510, according to the multi-dimensional calibration relationship in S220, the multi-dimensional calibration relationship can accurately determine the liquid density of the corresponding liquid in the pipe by using the driving energy consumption value and vibration frequency. Therefore, based on the real-time vibration frequency and real-time driving energy consumption value, the real-time density of the liquid currently flowing through the pipe is calculated based on the multi-dimensional calibration relationship.

[0126] For example, the real-time vibration frequency of Y Hz and the real-time drive energy consumption of 52mW are substituted into a two-dimensional lookup table or function established during the calibration phase. The lookup table determines the real-time density to be 1.040 g / cm³ based on the real-time drive energy consumption of 52mW and the real-time vibration frequency of Y Hz.

[0127] When there is no deviation component in the vibration frequency, the density can be calculated in real time through S510, thereby determining the final density.

[0128] In some embodiments of S520, the real-time density is re-determined by using the compensated real-time vibration frequency and multi-dimensional calibration relationship, and the re-determined real-time density is used as the final density to correct the real-time density.

[0129] Through the embodiments of S510 to S520, a correction is made when calculating the final density to avoid incorrectly interpreting the fluid resistance effect as a decrease in density. This solves the problem that the measurement deviation generated by the system is continuous and stable, but does not exceed the conventional single-point over-limit alarm threshold.

[0130] In some embodiments of the present invention, the density detection method may also include, but is not limited to, steps S501 to S502: Step S501: Monitor the changing trend of real-time drive energy consumption value, and based on the changing trend, confirm whether the real-time drive energy consumption value continues to deviate from the set expected range.

[0131] Step S502: When the real-time drive energy consumption value continues to deviate from the set expected energy range, an auxiliary warning signal is generated and issued.

[0132] In this embodiment, the application also independently monitors the changing trend of the drive energy consumption value, and determines whether the real-time drive energy consumption value continuously deviates from the set expected range based on the changing trend.

[0133] A sustained deviation from the set expected range refers to the real-time drive energy consumption value not falling within the expected range under normal operating conditions within the set time window. The set time window can be set according to demand, and its duration is not specifically limited in this application. The expected range under normal operating conditions can be a range of standard drive energy consumption values ​​corresponding to a specific density value, which can be determined based on actual liquid demand; no specific limitation is imposed in this application.

[0134] Even if the real-time density value calculated in the S500 is within the set density range and remains stable, when it is determined that the real-time drive energy consumption value continuously deviates from the set expected energy range, an auxiliary warning signal of "detector status abnormal" or "fluid resistance abnormal" will be generated and issued to alert the operator.

[0135] This auxiliary early warning signal, independent of the density over-limit alarm, is designed to alert operators that there may be increased deposits on the detector probe or that the current fluid resistance characteristics of the wine differ significantly from expectations. This provides a basis for early intervention before the problem damages product quality. This directly addresses the issue of reduced manual verification frequency due to management process changes, leading to wider losses due to systemic errors before they are detected, and provides automated, independent diagnostic capabilities.

[0136] In other words, it also independently monitors the changing trend of drive energy consumption. The system maintains a standard drive energy consumption range corresponding to a specific density value. Even if the calculated real-time density value is within the normal range and performs stably, if the real-time drive energy consumption value continuously deviates from its expected range under normal operating conditions (for example, for a specific density of wine, the normal drive energy consumption should be between 50mW and 60mW, but it is actually consistently above 80mW), an auxiliary warning of "sensor status abnormality" or "fluid resistance abnormality" will be triggered.

[0137] Through the embodiments S501 to S502, by independently monitoring anomalies in drive energy consumption values, operators are provided with an independent diagnostic basis to promptly identify problems when density values ​​appear normal but actually deviate, effectively mitigating the risks associated with reduced manual verification frequency. This method achieves strong immunity to hidden interference sources through in-depth interpretation of existing sensor information without adding any external hardware or altering the production process, representing a highly cost-effective solution.

[0138] Conventional density measurement schemes, when faced with probe surface deposits or differences in fluid resistance between the calibration liquid and the production liquid, cannot distinguish whether signal changes originate from density itself or fluid resistance, because their calibration relationship is based solely on the single correspondence between core vibration frequency and density. Therefore, when fluid resistance causes a persistent signal deviation, the calculated density value will also consistently deviate from the true value. However, because this deviation is stable and may not exceed the conventional single-point over-limit alarm threshold, the system cannot detect the problem in time. For example, if the sensor, after calibration with low fluid resistance, encounters a high-fluid-resistance liquid, its output signal may remain persistently low, incorrectly interpreted as a decrease in density. However, because the deviation is stable and within the "acceptable" range, the operator may mistakenly interpret it as normal. This system, however, allows for timely warnings before problems damage product quality—that is, when the measurement results are stable but exhibit persistent deviations. This mitigates the risks associated with reduced manual verification frequency and avoids the loss of an entire batch of products.

[0139] Please see Figure 2 , Figure 2 This is an optional flowchart of a method for detecting the density of flowing wine in a pipe according to another embodiment of this application. In some embodiments of the present invention, the parameters of fluid resistance conditions include: pump speed and liquid temperature. The density detection method may also include, but is not limited to, steps S600 to S640: In step S600, during the calibration operation, the temperature of the calibration liquid is adjusted according to multiple discontinuous temperature points to generate different fluid resistance conditions.

[0140] Step S610: Based on the density of the calibration liquid, the corresponding temperature points under different fluid resistance conditions, the corresponding vibration frequency, and the corresponding drive energy consumption value, a calibration dataset is formed.

[0141] In step S620, in the calibration dataset, when there are at least two sets of data with the same corresponding drive energy consumption value and the rest of the data are different, the at least two sets of data are taken as conflict data sets, all temperature points in the conflict data sets are extracted, and the temperature inflection point is calculated.

[0142] Step S630: Using the temperature inflection point as the boundary, the calibration dataset is divided into a high-temperature mapping dataset and a low-temperature mapping dataset. Based on the high-temperature mapping dataset and the low-temperature mapping dataset, a multi-dimensional calibration relationship for high temperature and a multi-dimensional calibration relationship for low temperature are established for each partition.

[0143] Step S640: Based on the real-time temperature and temperature inflection point of the liquid to be tested, call the corresponding high-temperature multi-dimensional calibration relationship or low-temperature multi-dimensional calibration relationship.

[0144] Due to the characteristics of the organic components within the wine, its fluid resistance does not change unidirectionally with temperature. Instead, it experiences a brief rebound as temperature rises within a certain range. This means that during calibration, when data is collected at several preset discontinuous temperature points, the driving energy required to maintain a constant amplitude of the vibrating density detector may be almost identical at two different temperature points. Although the driving power is the same, the wine density and the vibration frequency signal of the vibrating density detector at these two temperature points are different due to thermal expansion and contraction. This leads to data ambiguity when establishing calibration relationships, where one driving energy value corresponds to multiple density-vibration frequency states. This poses a potential problem for subsequent real-time density measurements.

[0145] In some embodiments of S600, as can be seen from S100, the parameters of the fluid resistance conditions include: pump speed and liquid temperature. Specifically, different fluid resistance conditions are created by adjusting the pump speed of the liquid pump and / or adjusting the liquid temperature in the pipeline.

[0146] During calibration, the liquid temperature is adjusted according to multiple preset discontinuous temperature points. The calibration liquid with known density is introduced into the vibrating density detector in sequence to form different fluid resistance conditions, so that the vibrating density detector can detect the calibration liquid under different fluid resistance conditions.

[0147] In some embodiments of S610, the vibration density detector detects the calibration liquid by using different fluid resistance conditions formed at different temperature points, and records the vibration frequency and driving energy consumption value corresponding to the current fluid resistance conditions.

[0148] A data set is formed based on the fluid resistance conditions containing temperature points, the vibration frequency corresponding to the conditions, and the driving energy consumption value corresponding to the conditions. Multiple data sets are obtained through different fluid resistance conditions formed at different temperature points. Based on the known density of the calibration liquid, the temperature point, the density of the calibration liquid, the corresponding vibration frequency at the temperature point, and the corresponding driving energy consumption value are recorded. The mapping dataset at this time is temporarily stored to form a calibration dataset.

[0149] For example, according to multiple preset discontinuous temperature points such as 20℃, 25℃, 30℃, and 35℃ This creates multiple different fluid resistance conditions. Through different fluid resistance conditions The corresponding vibration frequency is obtained. and the corresponding drive energy consumption value Multiple sets of data were obtained ( , , Based on the known density of the calibration liquid Record temperature points , calibrating the density of the liquid The corresponding vibration frequency at this temperature point and corresponding drive energy consumption values To form a calibration dataset ( , , , ), that is, the same calibration liquid is obtained, and A is recorded: (20℃, 1, , 1) and Record B: (25℃, 2, , 2).

[0150] In some embodiments of S620, the calibration dataset is analyzed to detect whether there are two or more data groups in the calibration dataset that have the same drive energy consumption value, but different corresponding temperature points, vibration frequencies and known densities. That is, it is to identify whether there are at least two data groups in the calibration dataset that have the same drive energy consumption, but other data are different.

[0151] For example, record A: ( , 1, , 1) and record B: ( , 2, 2, 2), where, ≠ , 1≠ 2, ≠ 2, 1 = 2.

[0152] When it is determined that at least two sets of data have the same corresponding drive energy consumption value, but all other data are different, it proves that the physical properties of the wine being tested exhibit non-unidirectionality in this region. Based on the calibration dataset, all data sets with the same corresponding drive energy consumption value are marked as conflicting data sets. The temperature points corresponding to the conflicting data sets are extracted, and the temperature inflection point is calculated based on all temperature points. .

[0153] The temperature inflection point can be simply taken as the average of all temperature values ​​in all conflicting data sets (e.g., ...). + Alternatively, based on the trend of the driving energy consumption value changing with temperature, the temperature at which the driving energy consumption value reaches a local extreme point can be determined as the inflection point through linear interpolation or curve fitting methods.

[0154] If there are no two sets of data with the same corresponding drive energy consumption value, but all other data are different, then the implementation schemes of S200 to S500 continue to be executed.

[0155] In some embodiments of S630, the temperature inflection point is used. Using this as a boundary, the entire calibration dataset is logically divided into two independent sub-datasets, forming a high-temperature mapping dataset and a low-temperature mapping dataset.

[0156] All temperature points are lower than or equal to the temperature inflection point The data sets were categorized into a low-temperature mapping dataset, below or equal to the temperature inflection point. The temperature is designated as the low-temperature zone. All temperature points are above the temperature inflection point. The data set is categorized into a high-temperature mapping dataset, above the temperature inflection point. The temperature is designated as the high-temperature zone.

[0157] The mapping datasets for low temperature and high temperature are processed separately, and their respective multi-dimensional calibration relationships are established independently.

[0158] Using the steps of S200, for each subset of data, a calibration relationship is determined by means of multivariate regression analysis or by constructing a two-dimensional lookup table, which includes the relationship between density, vibration frequency and driving energy consumption.

[0159] Two independent multidimensional calibration relationships are generated: a high-temperature multidimensional calibration relationship and a low-temperature multidimensional calibration relationship. The low-temperature multidimensional calibration relationship is for the low-temperature region (e.g., density_low temperature = ...). (Vibration frequency, driving energy consumption value)), the multi-dimensional calibration relationship for high temperature is for the high temperature region (e.g., density_high temperature = (Vibration frequency, driving energy consumption value)), where... and It can be a function or lookup table determined by calibration data.

[0160] These two calibration relationships can each accurately characterize the true density mapping law under the influence of fluid resistance on vibration frequency signal within the corresponding temperature range, thus effectively handling the situation where a single drive energy consumption value corresponds to multiple sets of density and frequency data.

[0161] In some embodiments of S640, when the production line is in normal operation, the real-time temperature, real-time vibration frequency and real-time drive energy consumption of the liquid to be tested are acquired.

[0162] For each set of received real-time data, first determine which temperature region (low temperature region or high temperature region) the current liquid to be measured belongs to based on the real-time temperature and temperature inflection point, and then select to call the corresponding multi-dimensional calibration relationship to calculate the density (multi-dimensional calibration relationship for high temperature and multi-dimensional calibration relationship for low temperature).

[0163] Specifically, when the real-time temperature is below or equal to the temperature inflection point, the low-temperature multi-dimensional calibration relationship is invoked; when the real-time temperature is above the temperature inflection point, the high-temperature multi-dimensional calibration relationship is invoked. In this way, even in regions where the relationship between fluid resistance and temperature is not unidirectional, the correct calibration logic can be selected for density compensation based on the actual physical state of the current temperature, thereby outputting an accurate real-time density value.

[0164] Through the embodiments S600 to S640, data acquisition is still performed at preset discrete temperature points during the calibration operation phase. In the data processing stage of the mapped dataset, it actively detects whether there are data groups in the dataset with the same driving energy consumption value but different temperatures, vibration frequencies, and true densities. Once such data conflicts are identified, it proves that the physical properties of the wine being tested exhibit non-unidirectionality in that region.

[0165] At this point, a single calibration relationship will not be forcibly fitted, nor will conflicting data be simply averaged or discarded. Instead, the temperature information corresponding to these conflicting data will be used to determine a temperature inflection point. This temperature inflection point reflects a physical fact, logically dividing the entire measurement range into two or more regions with different behavioral patterns. Subsequently, independent multi-dimensional calibration relationships will be established for each region (e.g., low-temperature and high-temperature regions). This means that the calibration relationship is no longer a single function, but rather the applicable calibration logic is dynamically selected based on the temperature range in which the liquid temperature falls.

[0166] During real-time measurement, the system acquires the real-time vibration frequency, driving power, and current temperature of the liquid. First, it determines the temperature range to which the liquid belongs, and then invokes the corresponding multi-dimensional calibration relationship. This ensures that even when the driving energy consumption may be the same but the physical state differs, the system can still use the additional, crucial contextual information of temperature to select the calibration logic that most accurately reflects the current physical reality for compensation.

[0167] In contrast, conventional density measurement schemes are usually sufficient and efficient with a single calibration relationship when dealing with liquids where the fluid resistance-temperature relationship is unidirectional. They do not require complex conflict detection and partitioning because the dataset does not contain ambiguity caused by different physical states corresponding to the same driving energy consumption value. However, for fruit wines, where fluid resistance varies non-unidirectionally, using a single calibration relationship will fail to correctly interpret the signal changes caused by both temperature and fluid resistance when the wine's temperature changes through areas where fluid resistance increases, leading to persistent measurement bias. This application, by accepting and utilizing this "non-unidirectionality" as a partitioning basis, allows the calibration relationship to faithfully characterize the wine's true physical behavior in different temperature regions, thus ensuring the accuracy of measurement results in the most critical areas. This addresses the problem that when performing multi-dimensional calibration on wines with a non-unidirectional fluid resistance-temperature relationship, the calibration operation collects data at multiple discontinuous temperature points, resulting in multiple different density and vibration frequency correspondences at a given driving energy consumption value, causing ambiguity in the calibration relationship and affecting the accuracy of subsequent measurements.

[0168] The following is a detailed description and explanation of the solutions in the embodiments of the present invention, using specific application examples: When using a substitute calibration liquid (known density 1.040 g / cm³) for calibration, the vibration frequency of the vibrating U-shaped density detector tube is recorded. Simultaneously, the driving energy consumption required to maintain a constant amplitude vibration of the U-shaped tube is monitored and recorded. To establish a multi-dimensional calibration relationship, multi-point calibration is performed to create different fluid resistance conditions. Under these conditions, a mapping dataset is formed based on the known density of the substitute calibration liquid (1.040 g / cm³). For example, at 20°C, the driving energy consumption of the substitute liquid is 55 mW, and the core vibration frequency is X Hz; at 30°C (with slightly different fluid resistance), the driving energy consumption is 52 mW, and the core vibration frequency is Y Hz. (X Hz, 55 mW) -> 1.040 g / cm³; (Y Hz, 52 mW) -> 1.040 g / cm³ These data points, along with data from other standard calibration points, are used to determine a two-dimensional calibration lookup table or a polynomial function: density = A × frequency + B × energy + C. This lookup table or function reflects the precise mapping between the core vibration frequency signal and the true density under different fluid resistances (characterized by the driving energy consumption value). Thus, even if there is a deposit layer on the probe surface, or if the calibration liquid has different fluid resistances than the production liquid, the calibration relationship can accurately separate the influence of fluid resistance on the frequency signal.

[0169] The production line was switched to process high-sweetness wine with a true density of 1.050 g / cm³. When this wine flowed through a probe in a vibrating U-shaped density detection tube with a deposit layer, the real-time vibration frequency Z Hz and real-time drive energy consumption of 85 mW were collected. Due to the high fluid resistance of the high-sweetness wine and the deposit layer on the probe, the 85 mW drive energy consumption was significantly higher than the 55 mW consumed by the substitute wine during calibration.

[0170] The real-time vibration frequency of Z Hz and the real-time drive energy consumption of 85 mW were substituted into the two-dimensional lookup table or function established during the calibration phase to analyze the real-time vibration frequency. Based on the 85 mW drive energy consumption, the deviation component caused by high fluid resistance was identified in the Z Hz frequency signal. According to the quantization relationship in the multi-dimensional calibration relationship, the real-time vibration frequency Z Hz was compensated and corrected to calculate the accurate real-time density value, resulting in a final density of 1.050 g / cm³. This avoids the erroneous interpretation of the frequency decrease caused by high fluid resistance as a density decrease.

[0171] The final density calculated by continuous monitoring is 1.050 g / cm³, which is within the set density range of 1.02 g / cm³ to 1.06 g / cm³, so it will not trigger a density over-limit alarm.

[0172] Simultaneously, the drive energy consumption value is monitored independently. The current drive energy consumption value (85mW) of the high-sweetness liquid is consistently higher than the expected range for liquids of the same density under normal operating conditions (e.g., it should normally be between 60mW and 70mW). Although the final density is "qualified", an auxiliary warning of "sensor status / fluid resistance abnormality" will be immediately triggered, such as displaying a red warning on the HMI interface and emitting a buzzer.

[0173] Upon seeing this auxiliary warning, even if the density curve appears smooth, the operator will realize there is a potential problem. They can immediately arrange for the sensor to be inspected, the deposits on the probe surface to be identified and cleaned, or an additional rapid manual sampling verification of the current batch of wine. In this way, problems are detected and resolved early, preventing the entire batch of wine from being scrapped due to substandard density, significantly reducing economic losses and the complexity of quality traceability.

[0174] Another embodiment of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the density detection method for the flowing wine in the pipeline described above. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0175] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0176] Please see Figure 3 , Figure 3 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 301 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 302 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 302 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 302 and is called and executed by the processor 301 to execute the density detection method for flowing wine in a pipe according to the embodiments of this application. Input / output interface 303 is used to implement information input and output; The communication interface 304 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 305 transmits information between various components of the device (e.g., processor 301, memory 302, input / output interface 303, and communication interface 304); The processor 301, memory 302, input / output interface 303 and communication interface 304 are connected to each other within the device via bus 305.

[0177] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the density detection method for the flowing wine in the pipeline described above.

[0178] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0179] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0180] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0181] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0182] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0183] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0184] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0185] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0186] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for detecting the density of flowing wine in a pipeline, characterized in that, The method includes: The calibration operation is performed to obtain the vibration frequency and driving energy consumption value generated by the vibration density detector in the calibration liquid under different fluid resistance conditions, and a mapping dataset is formed based on the density of the calibration liquid. Based on the mapping dataset, a multi-dimensional calibration relationship is established; wherein, the driving energy consumption value represents the fluid resistance, and the multi-dimensional calibration relationship represents the mapping law between the vibration frequency and the true density under different fluid resistances; When the production line is in normal operation, the real-time vibration frequency and real-time drive energy consumption value generated by the vibration density detector are obtained; Based on the real-time drive energy consumption value and the multi-dimensional calibration relationship, the real-time vibration frequency is analyzed. When it is determined that the real-time vibration frequency contains a deviation caused by fluid resistance, fluid resistance compensation is performed on the real-time vibration frequency. Based on the compensated real-time vibration frequency, the real-time driving energy consumption value, and the multi-dimensional calibration relationship, the final density is determined. When the final density is within the set density range, the final density is used as the density of the liquid to be tested. Specifically, by utilizing the quantization relationship in the multi-dimensional calibration relationship, fluid resistance compensation is performed on the real-time vibration frequency based on the change in the real-time drive energy consumption value, or the vibration frequency calibrated under the same fluid resistance condition is used as the compensated real-time vibration frequency to achieve fluid resistance compensation.

2. The density detection method according to claim 1, characterized in that, The process of obtaining the drive energy consumption value includes: When the production line is in the preset reference state, it is confirmed that the reference flow medium in the pipeline is stable, the damping effect on the vibration density detector is constant, and the vibration density detector is driven to vibrate with the set constant amplitude. The instantaneous power required to maintain constant vibration of the vibration density detector is obtained, and the required instantaneous power is used as the energy baseline to periodically update the energy baseline. Obtain the real-time total drive energy consumption value currently output by the vibration density detector, and calculate the energy consumption difference between the real-time total drive energy consumption value and the energy baseline of the current production cycle; When the energy consumption difference is within the set energy range, the energy consumption difference is used as the drive energy consumption value.

3. The density detection method according to claim 1, characterized in that, The process of establishing a multi-dimensional calibration relationship based on the density of the calibration liquid and the mapping dataset includes: Based on the driving energy consumption values ​​of the calibration liquid under different fluid resistance conditions, the influence of fluid resistance on the vibration frequency is captured and quantified to obtain the quantitative relationship; Based on the quantization relationship, the density of the calibration liquid, and the mapping dataset, a multi-dimensional calibration relationship is established that includes the relationship between the density, the vibration frequency, and the driving energy consumption value.

4. The density detection method according to claim 1, characterized in that, The step of analyzing the real-time vibration frequency based on the real-time drive energy consumption value and the multi-dimensional calibration relationship includes: The real-time drive energy consumption value is used as the input parameter of the multi-dimensional calibration relationship to obtain the drive energy consumption value and the vibration frequency during calibration under the same fluid resistance conditions. Based on the real-time drive energy consumption value, the drive energy consumption value during calibration, and the vibration frequency during calibration, identify whether the real-time vibration frequency contains a deviation component caused by fluid resistance; Specifically, by comparing the real-time drive energy consumption value, the drive energy consumption value during calibration (as a reference), and the vibration frequency during calibration (as a reference), the real-time drive energy consumption value and the drive energy consumption value during calibration are compared. Based on the vibration frequency during calibration, it is determined whether there is a change in the real-time vibration frequency, so as to identify whether there is a deviation component in the real-time vibration frequency.

5. The density detection method according to claim 1, characterized in that, The step of determining the final density based on the compensated real-time vibration frequency, the real-time driving energy consumption value, and the multi-dimensional calibration relationship includes: The real-time vibration frequency and the real-time driving energy consumption value are input into the multi-dimensional calibration relationship to calculate the real-time density; Based on the compensated real-time vibration frequency and the multi-dimensional calibration relationship, the real-time density is corrected to obtain the final density.

6. The density detection method according to claim 1, characterized in that, The method further includes: During the calibration operation, the temperature of the calibration liquid is adjusted according to multiple discontinuous temperature points to generate different fluid resistance conditions; A calibration dataset is formed based on the density of the calibration liquid, the corresponding temperature points under different fluid resistance conditions, the corresponding vibration frequency, and the corresponding drive energy consumption value. In the calibration dataset, when there are at least two sets of data with the same corresponding drive energy consumption value and the rest of the data are different, the at least two sets of data are regarded as conflict data sets, all the temperature points in the conflict data sets are extracted, and the temperature inflection point is calculated. Using the temperature inflection point as the boundary, the calibration dataset is divided into a high-temperature mapping dataset and a low-temperature mapping dataset. Based on the high-temperature mapping dataset and the low-temperature mapping dataset, the multi-dimensional calibration relationship for high temperature and the multi-dimensional calibration relationship for low temperature are established in the partition. Based on the real-time temperature of the liquid to be tested and the temperature inflection point, the corresponding multi-dimensional calibration relationship for high temperature or low temperature is invoked.

7. The density detection method according to claim 1, characterized in that, The method further includes: Monitor the real-time driving energy consumption value and, based on the trend, confirm whether the real-time driving energy consumption value continuously deviates from the set expected range. When the real-time drive energy consumption value continuously deviates from the set expected energy range, an auxiliary warning signal is generated and issued.

8. The density detection method according to claim 2, characterized in that, The method further includes: Monitor the long-term trend of the energy baseline to confirm whether the energy baseline continues to deviate from the historical normal range or shows an upward trend; When the energy baseline continuously deviates from the historical normal range of the set baseline or shows an upward trend, a warning signal for detector performance degradation is generated and issued.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the density detection method according to any one of claims 1 to 8.

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

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