A base liquor density detection method for an automated blending system and related apparatus

Abstract

The application belongs to the technical field of liquid density detection, and discloses a base liquor density detection method for an automatic blending system and related equipment. The method comprises the following steps: acquiring a solvent capacity identifier of current base liquor to be delivered and a residual risk identifier of the last batch of delivered base liquor, judging whether a preset pollution risk time sequence combination exists in a delivery sequence; if the pollution risk time sequence combination exists, delivering the current base liquor to be delivered at a preset flow rate, and collecting a first density measurement value in a first measurement period; then, making the liquid in the delivery pipeline stand still for a preset length of time; then, delivering the current base liquor to be delivered at the preset flow rate again, and collecting a second density measurement value in a second measurement period; calculating a density difference value according to the first density measurement value and the second density measurement value; according to a comparison result of the density difference value and a preset dissolution effect determination threshold, confirming the true intrinsic density of the current base liquor to be delivered; and thus the accuracy and reliability of density detection are improved.

Description

Technical Field

[0001] This application relates to the field of liquid density detection technology, and more specifically, to a method and related equipment for detecting the density of base liquor in an automated blending system. Background Technology

[0002] In automated liquid blending production, accurately measuring the density of the base liquor is crucial for ensuring the quality of the finished product. However, in practice, due to the physical properties of specific base liquors and adjustments to the production process, trace residues may form inside the equipment. When subsequent base liquors with solvent properties flow through these residues, they slowly dissolve and enter the liquid flow, interfering with the density detector's measurement results. This causes the density data acquired by the system to deviate from the true value of the base liquor. This deviation is often insidious and gradual, making it difficult to detect in a timely manner by existing anomaly detection mechanisms. Ultimately, it may lead to incorrect blending decisions and affect product quality.

[0003] For example, in one production plan, the system needed to process a high-viscosity, high-sugar base liquor. Due to its unique physical properties, when this base liquor flowed through the inner wall of the liquid delivery pipes and onto the surface of the density detector's sensing element, a thin, uniform film of residue would still form, even after a routine evacuation operation. In response to an order to increase production efficiency, production line managers, without noticing the presence of this residue film, adjusted the cleaning process time between batch changes in the control system, reducing it by 15%. This adjustment aimed to reduce non-productive time and accelerate the production cycle.

[0004] However, this shortened cleaning process resulted in insufficient rinsing intensity and chemical reaction time. This prevented the system from completely removing the residual film formed by the high-sugar base spirits on the surfaces of pipes and detectors. In subsequent production cycles, this incompletely removed film began to thicken slowly and imperceptibly. This accumulation process was gradual and, due to its microscopic scale and slow nature, did not trigger any of the system's existing physical or logical anomaly detection mechanisms.

[0005] Subsequently, production was switched to a completely different base spirit: a low-density, high-alcohol, highly distilled spirit. This base spirit exhibited significant solvent properties. When this new high-alcohol base spirit began flowing through pipes and density detectors previously covered with a film of accumulated sugar residue, it did not rapidly flush away the residue like a cleaning fluid, but instead began to slowly and continuously dissolve the film. As dissolution progressed, some of the dissolved sugar gradually entered the mainstream liquid. The dissolved sugar was not uniformly distributed throughout the liquid flow, but rather within the density detector's measurement area, causing the local density of the liquid to be higher than the true density of the high-alcohol base spirit. The density detector faithfully measured and reported this contaminated, higher-than-true density data. This data deviation was not caused by detector malfunction, but by localized changes in the composition of the measured liquid itself.

[0006] After receiving these excessively high density data, the blending control unit calculates based on the preset base liquor formula ratio. Since the data indicates that the current base liquor density is higher than expected, the system logic determines that the amount of base liquor injected has exceeded the requirement, and therefore automatically reduces the planned injection volume of the high-alcohol base liquor. More challenging is that this dissolution process is continuous and non-linear. This means that the values ​​reported by the density detector exhibit a slow, irregular drift, rather than a sudden jump. The magnitude of this drift, within a single batch production cycle, just does not exceed the system's set abnormal alarm threshold for single-batch base liquor material fluctuations. Therefore, the system's existing abnormal alarm logic cannot identify this as a potentially harmful deviation caused by contamination, but instead misjudges it as normal material fluctuations. Ultimately, because the actual injection ratio of high-alcohol base liquor is far lower than the formula requirements, the entire batch of blended product is forced to be scrapped due to substandard composition. The root cause is that when faced with this continuous and non-linear local density change caused by the dynamic dissolution of historical residues, the system cannot accurately distinguish between the inherent density of the base liquor and the density increase caused by this accidental contamination.

[0007] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0008] The purpose of this application is to provide a method and related equipment for detecting the density of base liquor in an automated blending system. The aim is to solve the problem that, during the automated liquid blending process, due to the physical characteristics of specific base liquors and adjustments to the production process, trace residues are formed inside the equipment. When subsequent base liquors with solvent properties flow through these residues, the residues slowly dissolve and enter the liquid flow, thereby interfering with the measurement results of the density detector. This causes the density data obtained by the system to deviate from the true value of the base liquor, which may ultimately lead to incorrect blending decisions and affect product quality.

[0009] In a first aspect, this application provides a method for detecting the density of base liquor in an automated blending system, the method comprising:

[0010] A1. Obtain the solvent capacity identifier of the base liquor to be transported and the residual risk identifier of the base liquor transported in the previous batch, in order to determine whether there is a preset combination of contamination risk time sequence in the transport sequence;

[0011] A2. When there is a preset combination of contamination risk in the delivery sequence, the base wine to be delivered is delivered at a preset flow rate, and the liquid density data of the base wine to be delivered is collected during the first measurement period to obtain the first density measurement value;

[0012] A3. After the first measurement period ends, the delivery of the currently delivered base wine is paused, and the liquid in the delivery pipeline is allowed to stand for a preset time.

[0013] A4. After the settling time is over, the base wine to be transported is transported again at a preset flow rate, and the liquid density data of the base wine to be transported during the second measurement period is collected to obtain the second density measurement value.

[0014] A5. Calculate the density difference based on the first density measurement value and the second density measurement value;

[0015] A6. Based on the comparison result between the density difference and the preset dissolution effect judgment threshold, the true inherent density of the base wine to be transported is confirmed.

[0016] Secondly, this application provides a base liquor density detection device for an automated blending system, the device comprising:

[0017] The sequence determination module is used to obtain the solvent capacity identifier of the base liquor to be transported and the residual risk identifier of the base liquor transported in the previous batch, in order to determine whether there is a preset combination of contamination risk time sequence in the transport sequence.

[0018] The first density acquisition module is used to transport the base wine to be transported at a preset flow rate when there is a preset combination of contamination risk in the transport sequence, and to acquire the liquid density data of the base wine to be transported during the first measurement period to obtain the first density measurement value.

[0019] The settling control module is used to pause the delivery of the currently delivered base wine after the first measurement period ends, so that the liquid in the delivery pipeline can stand for a preset time.

[0020] The second density acquisition module is used to transport the current base wine to be transported again at a preset flow rate after the settling time ends, and to acquire the liquid density data of the current base wine to be transported during the second measurement period to obtain the second density measurement value.

[0021] The difference calculation module is used to calculate the density difference based on the first density measurement value and the second density measurement value;

[0022] The density confirmation module is used to confirm the true inherent density of the base wine to be transported based on the comparison result between the density difference and a preset dissolution effect judgment threshold.

[0023] Thirdly, this application provides an electronic device including a processor and a memory, the memory storing a computer program executable by the processor, wherein when the processor executes the computer program, it performs the steps in the base liquor density detection method for an automated blending system as described above.

[0024] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the base liquor density detection method for an automated blending system as described above.

[0025] Beneficial Effects: This application provides a method and related equipment for detecting the density of base liquor in an automated blending system. By introducing a static dissolution process and two density measurements, and analyzing the difference, it can distinguish between the true inherent density of the base liquor and the apparent density affected by the dissolution of residues in the pipeline. This segmented measurement and dynamic judgment strategy overcomes the limitation of existing systems in being unable to identify continuous and nonlinear local density changes caused by the dynamic dissolution of residues, and avoids misjudging deviations caused by contamination as normal material fluctuations. Therefore, this application can provide more accurate base liquor density data, effectively preventing blending decision errors and product quality problems caused by density measurement errors, and significantly improving the reliability and product qualification rate of the automated blending system. Attached Figure Description

[0026] Figure 1 A flowchart of a method for detecting the density of base liquor in an automated blending system provided in this application.

[0027] Figure 2 This is a schematic diagram of a base wine density detection device for an automated blending system provided in this application.

[0028] Figure 3 A schematic diagram of the structure of the electronic device provided in this application.

[0029] Labeling Explanation: 1. Sequence Judgment Module; 2. First Density Acquisition Module; 3. Static Control Module; 4. Second Density Acquisition Module; 5. Difference Calculation Module; 6. Density Confirmation Module; 301. Processor; 302. Memory; 303. Communication Bus. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0031] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0032] Please refer to Figure 1 A method for detecting the density of base liquor in an automated blending system, as described in some embodiments of this application, includes:

[0033] A1. Obtain the solvent capacity identifier of the base liquor to be transported and the residual risk identifier of the previous batch of base liquor to be transported, in order to determine whether there is a preset combination of contamination risk time sequence in the transport sequence (i.e. the order sequence of transporting each batch of base liquor).

[0034] A2. When there is a preset combination of contamination risk time sequence in the delivery sequence, the base wine to be delivered is delivered at a preset flow rate, and the liquid density data of the base wine to be delivered is collected during the first measurement period to obtain the first density measurement value;

[0035] A3. After the first measurement period ends, stop the delivery of the base wine to be delivered and allow the liquid in the delivery pipeline to stand for a preset time.

[0036] A4. After the settling time is over, the base wine to be transported is transported again at a preset flow rate, and the liquid density data of the base wine to be transported during the second measurement period is collected to obtain the second density measurement value.

[0037] A5. Calculate the density difference based on the first and second density measurements;

[0038] A6. Based on the comparison between the density difference and the preset dissolution effect threshold, confirm the true inherent density of the base wine to be transported.

[0039] This application introduces solvent capacity labels and residual risk labels to predict potential contamination risks. When a risk is identified, segmented measurement and static treatment are used to effectively distinguish between the inherent density of the base liquor and the density deviation caused by the dissolution effect, thereby significantly improving the accuracy and reliability of density detection and avoiding blending errors caused by contamination.

[0040] The "solvent capacity rating" refers to the ability of the base liquor to dissolve residues in the pipeline. This rating can be a qualitative or quantitative indicator; for example, its solvent capacity rating can be determined based on the physicochemical properties of the base liquor, such as its alcohol content, acidity, and polarity. The stronger the solvent capacity, the higher the likelihood and efficiency of dissolving residues.

[0041] The "residue risk label" refers to the likelihood of residues forming in the transport pipeline from the previous batch of base liquor and its potential impact on subsequent base liquor density measurements. This label can be assessed based on characteristics such as viscosity, sugar content, and protein content of the previous batch of base liquor. For example, base liquor with high viscosity and high sugar content is more likely to form residues, and its residue risk label will be higher.

[0042] "Pollution risk timing combination" refers to a combination of base wines with specific solvent capacity labels and base wines with specific residue risk labels in terms of delivery timing. This combination indicates a higher risk that residues in the pipeline will dissolve and affect density measurements. For example, delivering a high-solvent-capacity base wine immediately after a high-residue-risk base wine constitutes a pollution risk timing combination.

[0043] This application provides a method for detecting the density of base liquor in an automated blending system. The core of this method is to identify and eliminate density measurement deviations caused by the dissolution effect through segmented measurement and static treatment.

[0044] Specifically, this method first obtains the solvent capacity identifier of the base liquor to be transported and the residual risk identifier of the previous batch of base liquor transported. The solvent capacity identifier can be assessed based on parameters such as the alcohol content and polarity of the base liquor to be transported; for example, base liquor with higher alcohol content generally has stronger solvent capacity. The residual risk identifier can be assessed based on parameters such as the viscosity and sugar content of the previous batch of base liquor transported; for example, base liquor with higher viscosity or sugar content has a greater risk of forming residues in the pipeline. After obtaining these identifiers, the system determines whether the transport sequence contains a preset combination of contamination risks. For example, if a base liquor with high solvent capacity is transported immediately after a base liquor with high residual risk, there may be a contamination risk.

[0045] When the system determines that the delivery sequence contains a preset contamination risk combination, it will initiate a special density detection process. First, the base liquor to be delivered is conveyed at a preset flow rate, and the liquid density data of the base liquor is collected during the first measurement period to obtain the first density measurement value. This preset flow rate can be a fixed value, for example, set to 0.5 m / s, to ensure stable liquid flow within the pipeline. The first measurement period can be set according to actual needs, for example, 10 seconds, to collect sufficient data for preliminary assessment.

[0046] After the first measurement period ends, the system will pause the delivery of the base liquor to be delivered, allowing the liquid in the delivery pipeline to stand for a preset time. This standing time can be a fixed value, for example, set to 60 seconds, to allow sufficient time for any residues that may be present in the pipeline to dissolve in the base liquor, thus ensuring that the dissolution effect occurs fully during the standing period.

[0047] After the settling period, the system will again deliver the base liquor to be delivered at a preset flow rate and collect the liquid density data of the base liquor during the second measurement period to obtain the second density measurement value. The second measurement period can be the same as the first measurement period, for example, 10 seconds, to ensure consistency of measurement conditions. The second measurement period (and the first measurement period) can also be determined based on the length of the delivery pipeline and the actual flow rate of the base liquor, ensuring that the base liquor for density measurement during the second measurement period is base liquor that has been settling in the delivery pipeline, thus more accurately reflecting the density change before and after settling.

[0048] Subsequently, the density difference was calculated based on the first and second density measurements. This density difference reflects the change in liquid density caused by the dissolution of residues during the settling period.

[0049] Finally, the true intrinsic density of the base wine to be transported is confirmed by comparing the density difference with a preset dissolution effect threshold. The dissolution effect threshold can be an empirical value, such as 0.001 g / cm³. If the density difference is greater than this threshold, it indicates a significant dissolution effect, in which case it may be necessary to correct either the first or second density measurement, or combine both to determine the true intrinsic density. If the density difference is not greater than this threshold, it indicates that the dissolution effect is not significant, and one of the measurements (e.g., the first density measurement) can be directly used as the true intrinsic density.

[0050] The base liquor density detection method proposed in this application effectively identifies and quantifies density measurement deviations caused by the dissolution of pipeline residues by introducing a pre-judgment mechanism for the risk of contamination during the transportation sequence and combining segmented density measurement with intermediate settling treatment. When the system identifies a potential contamination risk, density data is first collected during the first measurement period after the base liquor begins transportation, obtaining a first density measurement value. At this time, the dissolution effect may not yet be fully manifested or may be in its initial stage. Subsequently, by pausing transportation and allowing the base liquor to settle for a preset time, time is provided for the base liquor to fully dissolve the residues in the pipeline, allowing the dissolution effect to fully develop. After the settling period, the base liquor is transported again, and density data is collected during the second measurement period, obtaining a second density measurement value. At this time, the density value may change significantly due to the dissolution effect. By comparing these two measurements, i.e., calculating the density difference, the degree of influence of the dissolution effect can be quantified. Finally, based on the comparison result of this density difference with a preset dissolution effect judgment threshold, the system can intelligently determine whether a significant dissolution effect exists and thereby confirm the true inherent density of the base liquor. These steps work together to enable the system to distinguish between the true inherent density of the base liquor and the apparent density changes caused by contamination, thereby avoiding blending errors caused by inaccurate density data and ensuring product quality.

[0051] Compared to existing technologies, the base liquor density detection method of this application has significant advantages and innovations. Traditional methods often rely on a single density measurement or simple abnormal fluctuation detection, making it difficult to effectively identify and handle gradual density deviations caused by the slow dissolution of pipeline residues. For example, in existing technologies, when high-alcohol base liquor flows through pipelines with accumulated sugar residues, the dissolved sugar causes the density detector to report a slow, irregular drift. However, the magnitude of this drift may not exceed the system's set abnormal alarm threshold within a single batch production cycle, thus being misjudged as normal material fluctuations. This application, by introducing solvent capacity and residue risk indicators, achieves the prediction of potential contamination risks, which is not available in existing technologies. More importantly, by introducing a segmented measurement strategy of "first measurement period - standing - second measurement period" during the transportation process, this application can capture and quantify the impact of dissolution effects on density measurements at different time points. By calculating the density difference between the first and second density measurements and comparing it with a preset dissolution effect threshold, this application can accurately determine whether a significant dissolution effect exists and thus confirm the true inherent density of the base liquor. This method can effectively distinguish between the inherent density of the base liquor and the apparent density increase caused by contamination, thereby avoiding blending errors caused by inaccurate density data and significantly improving the accuracy and reliability of the automated blending system.

[0052] In some implementations, step A1 includes:

[0053] A101. Obtain the nominal basic information of the base liquor to be delivered and the base liquor delivered in the previous batch; the nominal basic information includes alcohol content, viscosity and sugar content;

[0054] A102. Based on the nominal basic information of the base liquor to be transported, determine the solvent capacity level of the base liquor to be transported, and use it as a solvent capacity identifier;

[0055] A103. Based on the nominal basic information of the base liquor already delivered in the previous batch, determine the residual risk level of the base liquor already delivered in the previous batch, and use it as a residual risk identifier;

[0056] A104. Combine the residual risk label of the base liquor that has been transported in the previous batch with the solvent capacity label of the base liquor to be transported in the current batch according to the transport sequence to obtain the time sequence combination to be verified.

[0057] A105. Compare the timing combination to be verified with the preset list of contamination risk timing combinations to determine whether the delivery sequence has a preset contamination risk timing combination.

[0058] Specifically, in step A101, the nominal basic information refers to the inherent property data of the base liquor under standard conditions, which may include alcohol content, viscosity, and sugar content. This information can be obtained from a pre-set base liquor database by querying identifiers such as the brand, name, and batch number of the base liquor. For example, when the system identifies the name of the base liquor to be transported, it can query its corresponding nominal basic data such as alcohol content, viscosity, and sugar content. For the base liquor that has already been transported, its nominal basic information can also be obtained in a similar way to understand the characteristics of any residues that may have been left on the inner wall of the pipeline.

[0059] In step A102, the solvent capacity level is determined based on the nominal basic information of the base liquor to be transported. For example, base liquors with higher alcohol content generally have stronger dissolving power, base liquors with lower viscosity have better fluidity, and sugar content also affects their ability to dissolve or dilute residues. By comprehensively analyzing this information, the ability of the base liquor to be transported to dissolve or dilute potential residues can be quantified and classified into different solvent capacity levels as solvent capacity identifiers. For example, the solvent capacity level of each combination of multiple alcohol content ranges, multiple viscosity ranges, and multiple sugar content ranges can be determined in advance through experiments, forming a solvent capacity level lookup table. Based on the range that the alcohol content, viscosity, and sugar content of the base liquor to be transported fall into, the corresponding solvent capacity level can be obtained by looking up the solvent capacity level lookup table.

[0060] In step A103, the residue risk level is determined based on the nominal basic information of the previous batch of base liquor already transported. For example, some base liquors, due to their component characteristics (such as high sugar content and high viscosity), are more likely to form residues on the inner wall of the pipeline after transport, or their residues may have a significant impact on the quality of subsequent base liquors. By analyzing information such as the alcohol content, viscosity, and sugar content of the previous batch of base liquor, the likelihood of residue formation in the pipeline and the degree of risk of contamination to subsequent base liquors can be assessed, and these can be classified into different residue risk levels as residue risk indicators. For example, the residue risk level of each combination of multiple alcohol content ranges, multiple viscosity ranges, and multiple sugar content ranges can be determined in advance through experiments, forming a residue risk level lookup table. Based on the range that the alcohol content, viscosity, and sugar content of the previous batch of base liquor falls into, the corresponding residue risk level can be obtained by looking up the residue risk level lookup table.

[0061] In step A104, the generation of the time sequence combination to be verified involves combining the residual risk identifier of the previous batch of base wine and the solvent capacity identifier of the current batch of base wine according to the actual delivery sequence. This combination reflects the potential interaction between the "residual characteristics of the previous batch of base wine" and the "solvent capacity of the current batch of base wine". For example, the time sequence combination to be verified can be represented by an array, which includes two elements: the first element is the residual risk identifier of the previous batch of base wine, and the second element is the solvent capacity identifier of the current batch of base wine.

[0062] In step A105, by comparing the generated time sequence combination to be verified with a preset list of contamination risk time sequence combinations, it can be determined whether the transport sequence contains a preset contamination risk time sequence combination. The preset list of contamination risk time sequence combinations can be established based on historical data, experimental results, or expert experience, and includes base wine transport sequence combinations known to cause contamination or quality impact. For example, if the previous batch was a high-sugar, high-viscosity base wine (high residue risk), while the current batch is a high-alcohol, high-solubility base wine (high solvent capacity), this combination may be listed as a high-contamination risk time sequence combination.

[0063] This application's solution refines step A1, making the assessment of whether a pre-defined contamination risk sequence exists in the transport sequence more accurate and systematic. First, by obtaining the nominal basic information of the base liquor, objective data support is provided for subsequent risk assessment. Second, the characteristics of the base liquor are transformed into quantifiable solvent capacity and residual risk levels, simplifying and standardizing the complex characteristics of the base liquor. Furthermore, by combining these two levels according to the transport sequence, the interactions between base liquors during actual transport can be simulated. Finally, by comparing with a pre-defined list of contamination risk sequence combinations, potential contamination risks can be effectively identified, providing a basis for subsequent density testing and quality control. This method avoids potential oversights arising from relying solely on experience and improves the accuracy of risk identification.

[0064] The above technical solution enables refined management of contamination risks during the base liquor transport sequence in automated blending systems. Specifically, by acquiring and analyzing the basic information labeled with the base liquor, the solvent capacity of the base liquor to be transported and the residual risk of the previous batch of base liquor can be accurately assessed. This allows for the systematic construction and verification of transport sequence combinations, effectively identifying specific sequence combinations that may lead to contamination. This method significantly improves the accuracy and reliability of contamination risk assessment, providing a more precise risk assessment basis for subsequent base liquor density testing, and helping to ensure the quality stability and safety of blended base liquor.

[0065] Preferably, step A2 may include:

[0066] A201. During the first measurement period, the base liquor to be transported is delivered at a preset flow rate, and the liquid density data of the base liquor to be transported is continuously collected to obtain the first time-series density data sequence.

[0067] A202. Perform fluctuation analysis on the first time-series density data sequence to identify and remove instantaneous abnormal fluctuation data in the first time-series density data sequence to obtain the first corrected density data sequence;

[0068] A203. Perform statistical processing on the first corrected density data sequence to obtain the first density measurement value.

[0069] In step A201, continuously collecting the liquid density data of the base liquor to be transported means that throughout the entire first measurement period, the density sensor continuously acquires the density readings of the base liquor at a preset sampling frequency, forming a time-series data set, namely the first time-series density data sequence. Its purpose is to capture the overall density changes of the base liquor in the initial stage of transport, providing sufficient raw data for subsequent data processing.

[0070] Furthermore, in step A202, fluctuation analysis is performed on the first time-series density data sequence. Various statistical or signal processing methods can be employed, such as moving average, median filtering, and Kalman filtering, to identify and locate transient abnormal fluctuations in the data sequence that deviate from the normal range. These abnormal fluctuations may be caused by factors such as sensor noise, air bubbles in the pipe, and transient flow rate changes. By removing these transient abnormal fluctuations, noise interference can be effectively eliminated, resulting in a more stable and accurate first corrected density data sequence that reflects the density change trend of the base liquor.

[0071] Specifically, in step A203, statistical processing is performed on the first corrected density data sequence. This may include calculating its mean, median, weighted average, or performing other forms of aggregation operations to obtain a representative first density measurement. For example, the arithmetic mean of the first corrected density data sequence can be calculated as the first density measurement to smooth the data to the greatest extent and reduce the impact of random errors. The aim is to extract stable and accurate base wine density values ​​from the corrected, more reliable data sequence.

[0072] Through the above technical solution, this application can significantly improve the accuracy and reliability of base liquor density detection in automated blending systems. Compared with directly collecting single or unprocessed density data, this solution effectively filters out transient noise and interference that may occur during transportation by introducing continuous acquisition of data sequences, fluctuation analysis, and outlier removal. This ensures that the obtained first density measurement value can more accurately and stably reflect the inherent density of the base liquor to be transported. This is crucial for subsequent density difference calculation and dissolution effect determination, thereby providing more accurate base liquor parameters for automated blending systems, avoiding blending ratio deviations caused by measurement errors, and ultimately ensuring the quality and stability of the final product.

[0073] In actual operation, the viscosity characteristics of different base liquors and the roughness of the inner wall of the conveying pipeline may vary. These factors will affect the adhesion of the base liquor to the inner wall of the pipeline, thus affecting the stability and accuracy of the liquid density data acquisition. Simply using a fixed preset flow rate may not be able to adequately adapt to these changes, resulting in the first time-series density data sequence failing to accurately reflect the true density of the base liquor.

[0074] Therefore, in some preferred embodiments, step A201 includes:

[0075] Obtain the viscosity information of the base liquor to be transported and the roughness information of the inner wall of the transport pipeline;

[0076] Based on the viscosity information of the base liquor to be transported and the roughness information of the inner wall of the transport pipeline, the adhesion parameters of the base liquor to be transported on the inner wall of the transport pipeline are determined.

[0077] Based on the adhesion parameters, the corresponding preset flow rate is obtained by matching from the preset flow rate database;

[0078] During the first measurement period, the base wine to be transported is delivered at a preset flow rate that is matched, and the liquid density data of the base wine to be transported is continuously collected to obtain the first time-series density data sequence.

[0079] Specifically, the viscosity information here can be the viscosity information in the nominal basic information, which reflects the resistance characteristics of liquid flow. The roughness information of the inner wall of the conveying pipeline refers to the degree of microscopic unevenness of the inner wall surface of the pipeline, which affects the interaction between the liquid and the pipeline wall. It can be obtained through pre-measurement or from pipeline engineering data. The adhesion parameter can be understood as the adhesion strength or trend of the base liquor to be conveyed on the inner wall of the conveying pipeline. It comprehensively considers the viscosity characteristics of the base liquor and the physical surface condition of the pipeline inner wall; this parameter can be estimated through experimental measurement, empirical formula calculation, or based on fluid dynamics models. Its purpose is to quantify the degree of interaction between the base liquor and the pipeline wall in order to determine a suitable conveying flow rate. In practical applications, the flow rate database is a pre-established collection that stores the mapping relationship between different adhesion parameters and corresponding optimized flow rates. This database can be constructed and optimized through a large amount of experimental data, simulation, or expert experience. Once the adhesion parameters of the base liquor to be conveyed are determined, the system will query this database to match a preset flow rate that can effectively reduce the wall effect and ensure the stability of density measurement.

[0080] This application's solution obtains the viscosity information of the base liquor to be transported and the roughness information of the inner wall of the transport pipeline, and determines the adhesion parameters of the base liquor on the inner wall of the pipeline based on this. This allows for the targeted matching of a preset flow rate from a flow rate database that is adapted to the current actual working conditions. Because this preset flow rate is dynamically adjusted according to the characteristics of the base liquor and the pipeline conditions, it effectively suppresses or compensates for fluid instability and density measurement errors caused by differences in the adhesion of the base liquor during the first measurement period. By optimizing the transport flow rate, it ensures that the base liquor flows more stably and uniformly in the density sensor area, thus making the collected first-time-series density data sequence more accurately reflect the true density of the base liquor, providing high-quality raw data for subsequent density measurement calculations. Through the above technical solution, this application can adaptively determine the optimal transport flow rate based on the actual physical characteristics of the base liquor to be transported and the condition of the transport pipeline. This significantly improves the accuracy and stability of liquid density data acquisition during the first measurement period, effectively avoiding measurement deviations caused by a fixed flow rate being unable to adapt to changing working conditions. Therefore, the obtained first density measurement value will be more accurate, laying a solid foundation for subsequent density difference calculation and confirmation of true inherent density, thereby improving the reliability and applicability of the base wine density detection method in the entire automated blending system.

[0081] The settling time can be a preset fixed value. However, if the settling time fails to fully consider the dissolution characteristics between the base liquor to be transported and the residue in the transport pipeline, it may lead to insufficient dissolution of the residue, thus affecting the accuracy of subsequent density measurements; or, if the settling time is set too long, it will reduce the overall detection efficiency. To address this, this application further proposes a scheme to optimize the aforementioned settling time to ensure sufficient dissolution of the residue and improve system operating efficiency; specifically, in some possible implementations, step A3 includes:

[0082] A301. Obtain the alcohol content and viscosity information of the base liquor to be transported, as well as the residue type information of the base liquor transported in the previous batch;

[0083] A302. Based on the alcohol content and viscosity information of the base liquor to be transported and the residue type information of the previous batch of base liquor, determine the dissolution rate parameter of the residue of the base liquor to be transported.

[0084] A303. Based on the dissolution rate parameter, the corresponding standing time is obtained by matching from the preset dissolution efficiency database;

[0085] A304. Suspend the delivery of the base wine to be delivered, and allow the liquid in the delivery pipeline to settle for the specified settling time.

[0086] Specifically, in step A301, the alcohol content and viscosity information can be the alcohol content and viscosity information from the nominal basic information, which directly affect the solvent capacity of the base liquor. The residue type information describes the characteristics of the previous batch of base liquor that may exist in the delivery pipeline, such as its main components and solubility. This information can be obtained by querying the brand of the base liquor or a preset database, and its purpose is to provide basic data for determining the subsequent dissolution rate parameters.

[0087] Furthermore, in step A302, the dissolution rate parameter quantifies the efficiency and speed at which the base liquor dissolves residues in the pipeline. It is an indicator that comprehensively considers the solvent capacity of the base liquor and the ease with which the residues dissolve. For example, the dissolution rate parameter of the base liquor to be transported for the residues can be determined in the following way: the dissolution rate of base liquors with different alcohol contents and viscosities and various typical residue types is measured in advance to form a dissolution rate parameter database; when it is necessary to determine the dissolution rate parameter in actual operation, the dissolution rate parameter database is queried based on the alcohol content information, viscosity information of the base liquor to be transported, and residue type information of the previous batch of base liquor transported to obtain the corresponding dissolution rate parameter.

[0088] Subsequently, in step A303, the corresponding settling time is obtained by matching the determined dissolution rate parameter from a preset dissolution efficiency database. This dissolution efficiency database pre-stores the correspondence between different dissolution rate parameters and optimal settling times; these relationships can be obtained through experimental testing, experience accumulation, or theoretical calculation. Through matching, a settling time that ensures sufficient dissolution without excessively prolonging the waiting time can be found for the current specific combination of base wine and residue.

[0089] Finally, in step A304, the system pauses the delivery of the base liquor to be delivered, allowing the liquid in the delivery pipeline to settle for the calculated settling time. This allows any residue in the delivery pipeline to fully dissolve within the precisely calculated time under the influence of the base liquor, providing a pure and uniform measurement environment for subsequent density measurements.

[0090] Through the above technical solution, this application can dynamically adjust the settling time of the liquid in the delivery pipeline according to the actual characteristics of the base liquor and residues. This not only ensures that the residues in the delivery pipeline are fully dissolved, thus significantly improving the accuracy and reliability of subsequent density measurements and avoiding errors caused by residue interference, but also effectively improves the overall operating efficiency and throughput of the automated blending system by avoiding unnecessary excessive settling time. Furthermore, this solution enhances the system's adaptability, enabling it to flexibly handle the complex dissolution relationships between different types of base liquor and residues, providing more refined and intelligent control for the automated blending process.

[0091] Preferably, step A4 may include:

[0092] A401. After the settling time ends, during the second measurement period, the base wine to be transported is delivered at a preset flow rate, and the liquid density data of the base wine to be transported is continuously collected to obtain the second time-series density data sequence.

[0093] A402. Perform fluctuation analysis on the second time-series density data sequence to identify and remove instantaneous abnormal fluctuation data in the second time-series density data sequence, and obtain the second corrected density data sequence;

[0094] A403. Perform statistical processing on the second corrected density data sequence to obtain the second density measurement value.

[0095] Specifically, the process of step A401 can be referred to step A201, and will not be repeated here.

[0096] The process of step A402 can be referred to step A202, and will not be described in detail here.

[0097] The process of step A403 can be referred to step A203, and will not be described in detail here.

[0098] Through the above technical solution, this application can significantly improve the accuracy and reliability of the second density measurement value. By continuously collecting data and performing fluctuation analysis and outlier data removal, transient interferences that may occur during the transportation process, such as bubbles, unstable flow rates, or sensor noise, can be effectively filtered out, thereby avoiding deviations in the measurement results caused by these factors. As a result, the obtained second density measurement value is more stable and accurate, thus improving the overall accuracy and robustness of the entire base liquor density detection method, ensuring that the automated blending system can operate based on more accurate density data.

[0099] Specifically, in step A5, the absolute value of the difference between the first density measurement value and the second density measurement value can be calculated as the density difference value.

[0100] The threshold for determining the dissolution effect can be a preset fixed value. However, different types of base liquor have different physicochemical properties such as alcohol content and viscosity, resulting in varying dissolution effects on residues in the transportation pipeline. If a single, fixed threshold for determining the dissolution effect is used, it may not accurately reflect the actual dissolution of different base liquors under specific transportation scenarios, thus affecting the accuracy of confirming the true intrinsic density.

[0101] Therefore, in some preferred embodiments, step A6 includes:

[0102] A601. Obtain the alcohol content information of the base liquor to be delivered;

[0103] A602. Based on the alcohol content information of the base liquor to be delivered, the corresponding dissolution effect judgment threshold is obtained by matching from the preset dissolution effect judgment threshold database;

[0104] A603. Compare the density difference with the matching dissolution effect determination threshold;

[0105] A604. When the density difference is greater than the dissolution effect judgment threshold, the first density measurement value is confirmed as the true inherent density of the base wine to be transported.

[0106] A605. When the density difference is not greater than the dissolution effect judgment threshold, the true inherent density of the base wine to be transported is determined by combining the first density measurement value and the second density measurement value.

[0107] Specifically, in step A601, the alcohol content information can be the alcohol content information from the nominal basic information. This alcohol content information is one of the key parameters characterizing the physicochemical properties of the base liquor and has a significant impact on the solubility of the base liquor.

[0108] In step A602, a pre-established database of dissolution effect judgment thresholds is created, storing the mapping relationship between different alcohol content information and corresponding dissolution effect judgment thresholds. For example, through experimental testing or historical data analysis, the possible dissolution effects of base liquors with different alcohol content ranges under specific residue and pipeline conditions can be determined, and corresponding judgment thresholds can be set accordingly. When the alcohol content information of the base liquor to be transported is obtained, the system will query the database to match the most suitable dissolution effect judgment threshold for the characteristics of the current base liquor.

[0109] In step A603, the density difference calculated in step A5 is compared with the dissolution effect determination threshold obtained in step A602. This comparison result is the key basis for determining whether the dissolution effect is significant.

[0110] In step A604, when the density difference exceeds the dissolution effect determination threshold, it indicates that during the settling process, the base liquor to be transported has a significant dissolution effect on the residues in the transport pipeline, resulting in a large change in liquid density. In this case, the first density measurement value, i.e., the density data collected before the dissolution effect has fully manifested or stabilized, is considered to be closer to the true intrinsic density of the base liquor to be transported.

[0111] In step A605, when the density difference is not greater than the dissolution effect determination threshold, it indicates that the dissolution effect is relatively small or has stabilized. At this time, both the first density measurement and the second density measurement are close to the true intrinsic density, and either one can be used as the true intrinsic density. However, compared to using only either the first density measurement or the second density measurement as the true intrinsic density, a more accurate and robust true intrinsic density can be obtained by performing a weighted average or other statistical processing on the two measurements.

[0112] Through the above technical solution, this application can dynamically adjust the dissolution effect judgment threshold according to the alcohol content information of different base liquors, significantly improving the adaptability of the base liquor density detection method to different base liquor types and transportation scenarios. This avoids the problems of misjudgment or insufficient accuracy that may result from using a fixed threshold, ensuring a more accurate and reliable confirmation of the true inherent density of the base liquor to be transported, even in situations with contamination risk. Furthermore, by introducing an intelligent judgment mechanism based on the comparison of density difference and dynamic threshold, the processing method for density measurements can be selected more scientifically, further improving the accuracy and robustness of density detection results and providing a more reliable basis for base liquor quality control in automated blending systems.

[0113] Preferably, step A605 may include:

[0114] Obtain the adhesion parameters of the base liquor to be transported on the inner wall of the transport pipeline;

[0115] Based on the adhesion parameters, the corresponding weighting coefficients are obtained by matching from a preset density-weighted database;

[0116] The true intrinsic density of the base wine to be transported is calculated based on the first density measurement, the second density measurement, and the weighting coefficient.

[0117] Specifically, the method for obtaining adhesion parameters can be found in the corresponding steps in step A201 above, and will not be repeated here.

[0118] The density-weighted database is a pre-built database that stores the correspondence between adhesion parameters and weighting coefficients. This database can be constructed using extensive experimental data, simulations, or empirical rules. For example, a high adhesion parameter may mean that residue on the pipe wall has a greater impact on the first density measurement. In this case, the weight of the first density measurement should be relatively low, while the weight of the second density measurement should be relatively high, and vice versa. By matching the corresponding weighting coefficients, the first and second density measurements can be reasonably allocated according to the actual adhesion situation when calculating the true intrinsic density, thereby improving the accuracy of the calculation results.

[0119] In practical applications, the true inherent density of the base liquor to be transported is calculated based on the first density measurement, the second density measurement, and weighting coefficients. This can be achieved using a weighted average method or other appropriate mathematical models. For example, the true inherent density can be expressed as: True Inherent Density = W1 * First Density Measurement + W2 * Second Density Measurement, where W1 and W2 are weighting coefficients obtained based on adhesion parameters, and W1 + W2 = 1. This calculation method effectively balances the two measurements and corrects the results based on adhesion parameters to more accurately reflect the true inherent density of the base liquor.

[0120] This application's solution introduces the adhesion parameter of the base liquor to be transported on the inner wall of the transport pipeline, and obtains the corresponding weighting coefficient based on this parameter from a preset density weighting database. This weighted calculation of the first and second density measurements determines the true inherent density of the base liquor. The adhesion of the base liquor to the inner wall of the transport pipeline affects its flow state within the pipeline and the potential dissolution effect of residues, thus causing varying degrees of deviation in the density measurement results. By quantifying this adhesion and converting it into weighting coefficients, this application can more precisely adjust the contribution ratio of the first and second density measurements in the final density determination. This weighted processing based on the adhesion parameter allows for more accurate elimination or reduction of measurement errors caused by the adhesion effect on the inner wall of the pipeline when the density difference is not greater than the dissolution effect threshold, thereby obtaining results closer to the true inherent density of the base liquor.

[0121] Through the above technical solution, this application overcomes the potential inaccuracies of traditional methods when combining the first and second density measurements. By considering the adhesion parameter of the base liquor to the inner wall of the conveying pipeline and dynamically adjusting the weighting coefficient of the two measurements accordingly, the calculation of the true intrinsic density becomes more scientific and accurate. This method effectively compensates for measurement deviations caused by the adhesion characteristics of the base liquor, especially when the dissolution effect is insignificant. It significantly improves the accuracy and reliability of base liquor density detection, providing more precise base liquor density data for automated blending systems, thereby ensuring precise control of the blending process and the quality stability of the final product.

[0122] refer to Figure 2 This application provides a base liquor density detection device for an automated blending system, the device comprising:

[0123] Sequence determination module 1 is used to obtain the solvent capacity identifier of the base liquor to be transported and the residual risk identifier of the base liquor transported in the previous batch, in order to determine whether there is a preset combination of contamination risk time sequence in the transport sequence (for details, please refer to step A1 above).

[0124] The first density acquisition module 2 is used to transport the base wine to be transported at a preset flow rate when there is a preset combination of contamination risk in the transport sequence, and to acquire the liquid density data of the base wine to be transported during the first measurement period to obtain the first density measurement value (the specific process can be referred to step A2 above).

[0125] The settling control module 3 is used to pause the delivery of the base wine to be delivered after the first measurement period ends, so that the liquid in the delivery pipeline can be settling for a preset time (the specific process can be referred to step A3 above).

[0126] The second density acquisition module 4 is used to transport the base wine to be transported again at a preset flow rate after the settling time ends, and to collect the liquid density data of the base wine to be transported during the second measurement period to obtain the second density measurement value (the specific process can be referred to step A4 above).

[0127] The difference calculation module 5 is used to calculate the density difference based on the first density measurement value and the second density measurement value (for details, please refer to step A5 above).

[0128] The density confirmation module 6 is used to confirm the true inherent density of the base wine to be transported based on the comparison result between the density difference and the preset dissolution effect judgment threshold (for details, please refer to step A6 above).

[0129] Please refer to Figure 3This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device includes a processor 301 and a memory 302. The processor 301 and the memory 302 are interconnected and communicate with each other via a communication bus 303 and / or other forms of connection mechanisms (not shown). The memory 302 stores a computer program executable by the processor 301. When the electronic device is running, the processor 301 executes the computer program to perform a base liquor density detection method for an automated blending system in any optional implementation of the above embodiments, to achieve the following functions: obtaining the solvent capacity identifier of the currently transported base liquor and the residual risk identifier of the previous batch of transported base liquor, to determine whether there is a preset contamination in the transport sequence. The system employs a pre-defined contamination risk time sequence combination method. When a pre-defined contamination risk time sequence combination exists in the transport sequence, the base liquor to be transported is transported at a pre-defined flow rate, and the liquid density data of the base liquor to be transported is collected during the first measurement period to obtain a first density measurement value. After the first measurement period ends, the transport of the base liquor to be transported is paused, and the liquid in the transport pipeline is allowed to stand for a pre-defined time. After the standing time ends, the base liquor to be transported is transported again at a pre-defined flow rate, and the liquid density data of the base liquor to be transported is collected during the second measurement period to obtain a second density measurement value. The density difference is calculated based on the first density measurement value and the second density measurement value. The true intrinsic density of the base liquor to be transported is confirmed by comparing the density difference with a pre-defined dissolution effect judgment threshold.

[0130] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it executes the base liquor density detection method for an automated blending system in any optional implementation of the above embodiments to achieve the following functions: acquiring the solvent capacity identifier of the current base liquor to be transported and the residual risk identifier of the previous batch of base liquor already transported, to determine whether there is a preset contamination risk time sequence combination in the transport sequence; when there is a preset contamination risk time sequence combination in the transport sequence, transporting the current base liquor to be transported at a preset flow rate and collecting the liquid density data of the current base liquor to be transported during a first measurement period to obtain a first density measurement value; after the first measurement period ends, pausing the transport of the current base liquor to be transported and allowing the liquid in the transport pipeline to stand for a preset time; after the standing time ends, transporting the current base liquor to be transported again at a preset flow rate and collecting the liquid density data of the current base liquor to be transported during a second measurement period to obtain a second density measurement value; calculating the density difference based on the first density measurement value and the second density measurement value; and confirming the true intrinsic density of the current base liquor to be transported based on the comparison result of the density difference with a preset solubility effect judgment threshold. The computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0131] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A base spirit density detection method for an automated blending system, characterized by, The method comprises: A1. Obtain the solvent capacity identifier of the current base liquor to be delivered and the residual risk identifier of the last batch of delivered base liquor, to determine whether the delivery sequence has a preset pollution risk time sequence combination; A2. When the delivery sequence has a preset pollution risk time sequence combination, deliver the current base liquor to be delivered at a preset flow rate, and collect the liquid density data of the current base liquor to be delivered within a first measurement period to obtain a first density measurement value; A3. After the end of the first measurement period, pause the delivery of the current base liquor to be delivered, and let the liquid in the delivery pipeline stand for a preset length of time; A4. After the end of the standing time, deliver the current base liquor to be delivered again at a preset flow rate, and collect the liquid density data of the current base liquor to be delivered within a second measurement period to obtain a second density measurement value; A5. Calculate the density difference value according to the first density measurement value and the second density measurement value; A6. According to the comparison result of the density difference value and the preset dissolution effect determination threshold, confirm the true intrinsic density of the current base liquor to be delivered.

2. A base spirit density detection method for an automated blending system as claimed in claim 1, wherein, Step A1 comprises: A101. Obtain the nominal basic information of the current base liquor to be delivered and the last batch of delivered base liquor; the nominal basic information includes alcohol content information, viscosity information and sugar content information; A102. Determine the solvent capacity level of the current base liquor to be delivered according to the nominal basic information of the current base liquor to be delivered, as the solvent capacity identifier; A103. Determine the residual risk level of the last batch of delivered base liquor according to the nominal basic information of the last batch of delivered base liquor, as the residual risk identifier; A104. Combine the residual risk identifier of the last batch of delivered base liquor and the solvent capacity identifier of the current base liquor to be delivered according to the delivery time sequence to obtain a to-be-verified time sequence combination; A105. Compare the to-be-verified time sequence combination with a preset pollution risk time sequence combination list to determine whether the delivery sequence has a preset pollution risk time sequence combination.

3. A base spirit density detection method for an automated blending system as claimed in claim 1, wherein, Step A2 comprises: A201. Deliver the current base liquor to be delivered at a preset flow rate within the first measurement period, and continuously collect the liquid density data of the current base liquor to be delivered to obtain a first time sequence density data sequence; A202. Perform fluctuation analysis on the first time sequence density data sequence, identify and eliminate transient abnormal fluctuation data in the first time sequence density data sequence to obtain a first corrected density data sequence; A203. Perform statistical processing on the first corrected density data sequence to obtain the first density measurement value.

4. The base spirit density detection method for an automated blending system of claim 1, wherein, Step A3 comprises: A301. Obtain the alcohol content information and viscosity information of the current base liquor to be delivered and the residual type information of the last batch of delivered base liquor; A302. Determine the dissolution rate parameter of the current base liquor to be delivered for the residual according to the alcohol content information and viscosity information of the current base liquor to be delivered and the residual type information of the last batch of delivered base liquor; A303. According to the dissolution rate parameter, match the corresponding standing time from the preset dissolution efficiency database; A304. pause the delivery of the current base wine to be delivered, and let the liquid in the delivery pipeline stand for a standing time period matched.

5. A base spirit density detection method for an automated blending system as claimed in claim 1, wherein, Step A4 comprises: A401. after the standing time period ends, deliver the current base wine to be delivered at the preset flow rate within the second measurement time period, and continuously collect the liquid density data of the current base wine to be delivered to obtain a second time-series density data sequence; A402. perform fluctuation analysis on the second time-series density data sequence, identify and eliminate transient abnormal fluctuation data in the second time-series density data sequence to obtain a second corrected density data sequence; A403. perform statistical processing on the second corrected density data sequence to obtain the second density measurement value.

6. A base spirit density detection method for an automated blending system as defined in claim 1, wherein, Step A6 comprises: A601. obtain the alcohol content information of the current base wine to be delivered; A602. according to the alcohol content information of the current base wine to be delivered, match a corresponding dissolution effect determination threshold value from a preset dissolution effect determination threshold value database; A603. compare the density difference value with the matched dissolution effect determination threshold value; A604. when the density difference value is greater than the dissolution effect determination threshold value, confirm that the first density measurement value is the true intrinsic density of the current base wine to be delivered; A605. when the density difference value is not greater than the dissolution effect determination threshold value, determine the true intrinsic density of the current base wine to be delivered by comprehensively considering the first density measurement value and the second density measurement value.

7. A base spirit density detection method for an automated blending system as claimed in claim 6, wherein, Step A605 comprises: obtain the adhesion parameter of the current base wine to be delivered on the inner wall of the delivery pipeline; according to the adhesion parameter, match a corresponding weighting coefficient from a preset density weighting database; according to the first density measurement value, the second density measurement value, and the weighting coefficient, calculate the true intrinsic density of the current base wine to be delivered.

8. A base spirit density detection device for an automated blending system, characterized by, The device comprises: a sequence judgment module, configured to obtain a solvent capacity identifier of a current base wine to be delivered and a residual risk identifier of a last batch of delivered base wine, and to judge whether a delivery sequence exists a preset pollution risk time sequence combination; a first density collection module, configured to, when the delivery sequence exists the preset pollution risk time sequence combination, deliver the current base wine to be delivered at a preset flow rate, and collect liquid density data of the current base wine to be delivered within a first measurement time period to obtain a first density measurement value; a standing control module, configured to, after the first measurement time period ends, pause the delivery of the current base wine to be delivered, and let the liquid in the delivery pipeline stand for a preset time period; a second density collection module, configured to, after the standing time period ends, deliver the current base wine to be delivered at the preset flow rate again, and collect liquid density data of the current base wine to be delivered within a second measurement time period to obtain a second density measurement value; a difference calculation module, configured to calculate a density difference value according to the first density measurement value and the second density measurement value; a density confirmation module, configured to confirm the true intrinsic density of the current base wine to be delivered according to a comparison result of the density difference value and a preset dissolution effect determination threshold value.

9. An electronic device, comprising: A computer program product comprising a computer readable medium having stored thereon computer program means which, when executed by a processor, cause the processor to perform the steps of the method for detecting the density of a base spirit for an automated blending system according to any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to perform the steps of the method for detecting the density of a base spirit for an automated blending system according to any one of claims 1 to 7.

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