A condition monitoring and early warning system for a sargassum beverage production line

Abstract

The present invention relates to the technical field of state supervision, and specifically to a state monitoring and early warning system for a Sargassum fusiformis beverage production line, comprising a data acquisition and processing module, for collecting data from various links of the beverage production line; a state monitoring module, for accurately monitoring the state of each link of beverage production by constructing a multi-level monitoring mechanism; a feedback adjustment module, for performing dynamic feedback adjustment of production parameters according to monitoring results; the present invention constructs a dynamic monitoring model combining time series difference and time-space difference analysis, which can detect potential anomalies in the production process in a timely manner and avoid product quality problems caused by untimely monitoring; through real-time monitoring result input, production parameters can be dynamically adjusted, from basic adjustment in early production to power adjustment in intermediate links, and then to quality optimization in finished product links, thereby realizing a dynamic adjustment mechanism of closed-loop control and ensuring production stability and product consistency.

Description

Technical Field

[0001] The invention relates to the technical field of state supervision, and in particular to a state monitoring and early warning system for a sargassum beverage production line. Background Art

[0002] During the operation of the Sargassum beverage production line, in order to ensure production stability, efficiency and product quality, status detection indicators are particularly important. However:

[0003] Most of the existing beverage production line monitoring systems are based on single features or simple optical data, which makes it difficult to achieve refined monitoring of beverage ingredients and quality. Especially in the face of complex production environments under light sources of different wavelengths, the accuracy and efficiency of the existing systems are not ideal.

[0004] Traditional beverage production monitoring systems rely on fixed thresholds and single-layer monitoring, and lack timely capture of continuous changes in beverage production status, resulting in the inability to provide timely warnings when production is abnormal, which may affect product quality.

[0005] Traditional production line monitoring systems are usually passive monitoring systems, which make it difficult to dynamically adjust production parameters based on real-time monitoring data, leading to the accumulation of abnormal problems and affecting the final product qualification rate. Summary of the invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a state monitoring and early warning system for a Sargassum fusiformis beverage production line, comprising:

[0008] A data acquisition and processing module collects optical data of beverage samples by dynamically selecting light sources of different wavelengths and constructs a data set, constructs a multi-dimensional feature matrix based on the data set and performs feature extraction to construct the extracted features into a new feature vector;

[0009] And, the status monitoring module monitors the status of each link of beverage production by building a multi-level monitoring mechanism, specifically:

[0010] The first layer monitors the beverage at the earliest production stage through the basic eigenvalues ​​in the eigenvector;

[0011] The second layer receives the time series data of the absorbance gradient and the mean transmittance from the first layer, and monitors it through time series difference analysis to identify the continuous changes of beverage ingredients during the production process;

[0012] The third layer receives the spatiotemporal differential analysis results and cumulative differential analysis results from the second layer to conduct in-depth monitoring of the turbidity of the finished beverage and identify the status of the finished beverage. The output results of each layer provide data input for the next layer, and monitor the status of the beverage production link layer by layer.

[0013] Also includes:

[0014] The feedback adjustment module is used to take the judgment information on the beverage production status from the three-layer monitoring mechanism as input, and the dynamic adjustment results of the production parameters as output, to perform basic adjustments to the early production links, adjustments to the intermediate links in the production process, and dynamic feedback adjustments to the quality of the final product.

[0015] As a preferred solution of the state monitoring and early warning system of the Sargassum fusiformis beverage production line of the present invention, the optical data of the beverage sample collected by dynamically selecting light sources of different wavelengths is specifically as follows:

[0016] When the beverage production link is the first production stage, the length is selected A light source with a wavelength of irradiates the beverage sample;

[0017] When the beverage production stage is the second production stage, the length is selected A light source with a wavelength of irradiates the beverage sample;

[0018] When the beverage production link is the third production stage, the length is selected A light source with a wavelength of irradiates the beverage sample;

[0019] According to different wavelengths, data from different beverage production links are collected, including the concentration of beverage samples and the transmittance of beverage samples.

[0020] The concentration of the beverage sample,

[0021]

[0022] The light transmittance of the beverage sample,

[0023]

[0024] in, Indicated in wavelength The absorbance under Indicated in wavelength The molar absorption coefficient under represents the distance traveled by light in the beverage sample, Indicated in wavelength The concentration of the beverage sample was Indicated in wavelength The light transmittance of the beverage samples below.

[0025] As a preferred solution of the state monitoring and early warning system of the Sargassum fusiformis beverage production line described in the present invention, the feature extraction of the multi-dimensional feature matrix is ​​specifically as follows:

[0026]

[0027] in, Indicates wavelength The rate of change of absorbance with time under , Respectively represent Time and Data collected at all times;

[0028]

[0029] in, Indicates wavelength The average light transmittance at different times under Indicates wavelength At the moment The light transmittance, Indicates the upper limit of the collection time period;

[0030] The extracted features are constructed as new feature vectors, then we have:

[0031]

[0032] in, represents the constructed feature vector, Indicates wavelength The rate of change of absorbance with time under Indicates wavelength The average light transmittance at different times.

[0033] As a preferred solution of the state monitoring and early warning system of the Sargassum fusiformis beverage production line of the present invention, the monitoring of the beverage in the earliest production link is specifically as follows:

[0034] According to the absorbance gradient in the eigenvector And the average transmittance Detect the changing trends in the earliest stages of beverage production, specifically:

[0035] Setting the absorbance gradient benchmark threshold for the earliest production stage of beverages And the transmittance mean threshold ,

[0036] If the absorbance gradient and transmittance mean in the feature vector are compared with the set threshold and satisfy the formula When it is on, it means that the status of the earliest production link of the beverage is abnormal.

[0037] As a preferred solution of the state monitoring and early warning system of the Sargassum fusiformis beverage production line of the present invention, the continuous changes of the beverage ingredients during the production process are specifically identified as follows:

[0038]

[0039] in, express The eigenvalues ​​in the eigenvector at time instant, express The eigenvalues ​​in the eigenvector at time instant, Represents the difference result of the eigenvalues ​​at two consecutive time points;

[0040] Cumulative difference analysis is performed based on a moving window, specifically:

[0041]

[0042] in, Indicates the size of the moving window, which is the length of two time periods and is set by the implementer according to the actual application scenario. Indicates the past The cumulative difference of all eigenvalues ​​at each time point, Indicates The characteristic value is The difference result at time, represents the total number of eigenvalues ​​in the eigenvector, Indicates the upper limit of the time period for cumulative analysis;

[0043] According to the set standard threshold Determine whether the current stage of beverage production has experienced abnormal state changes, if yes,

[0044] If the cumulative difference result meets the standard threshold, the formula , it means that there is no abnormality in the beverage production status at the current stage;

[0045] If the cumulative difference result meets the standard threshold, the formula , it means that the beverage production status at the current stage is abnormal.

[0046] As a preferred solution of the state monitoring and early warning system of the Sargassum fusiformis beverage production line of the present invention, the state of the finished beverage product is specifically identified as follows:

[0047] For beverages in normal production status, in-depth monitoring of the turbidity of the finished beverage is carried out.

[0048] Set a specific wavelength The light source is used to irradiate the standard beverage product to obtain the absorbance of the standard beverage product. And light transmittance , and construct the linear relationship between the turbidity of the standard beverage product and the absorbance and transmittance, then,

[0049]

[0050] in, Indicates the turbidity of standard beverage products. Indicates a specific wavelength The absorbance of the finished standard beverage is as follows: Indicates a specific wavelength The light transmittance of the finished standard beverage is as follows: , Respectively represent the slope and intercept in the linear relationship function;

[0051] According to the constructed linear relationship function, and set a certain slope and intercept, use the light source with the same wavelength and angle to illuminate the finished beverage, and record the absorbance and transmittance of each finished beverage under the light source, and use the result as the input of the linear relationship function. Finally, monitor the status of the finished beverage according to the output of the function, then,

[0052] Absorbance input for linear relationship function And the transmittance input , the output is:

[0053]

[0054] At the same time, the average turbidity of the finished beverage in the current batch is calculated, then,

[0055]

[0056] in, Indicates a specific wavelength The absorbance of the current finished beverage is Indicates a specific wavelength The light transmittance of the current finished beverage is , They represent the slope and intercept of the linear relationship function respectively. Indicates the total number of beverage products involved in monitoring in the current batch. Indicates the turbidity of the current finished beverage. Indicates The turbidity of the finished beverage, Indicates the average turbidity of the current batch of finished beverages, which is used to determine whether the current finished beverages are qualified. Specifically:

[0057] When the calculated turbidity of the current beverage product is compared with the turbidity of the standard beverage product, the formula is satisfied. When , it means that the current beverage product is qualified;

[0058] When the calculated turbidity of the current beverage product is compared with the turbidity of the standard beverage product, the formula is satisfied. , it means that the current finished beverage is unqualified.

[0059] As a preferred solution of the state monitoring and early warning system of the Sargassum fusiformis beverage production line of the present invention, the basic adjustment of the early production link is as follows:

[0060] If the earliest monitored production link status is normal production, maintain the current production conditions to produce beverages;

[0061] If the earliest production link status monitored is abnormal, dynamic basic adjustment is performed according to the monitoring results of absorbance gradient and transmittance average, then,

[0062] When the earliest production link is abnormal, dynamic basic adjustment is achieved by extending the raw material mixing time, specifically:

[0063]

[0064] in, Indicates the mixing time of raw materials before adjustment, Indicates the adjusted raw material mixing time, represents the basic adjustment coefficient, represents the absorbance gradient, Indicates the absorbance gradient benchmark threshold of the earliest production stage of the beverage.

[0065] As a preferred solution of the state monitoring and early warning system of the Sargassum fusiformis beverage production line of the present invention, the adjustment of the intermediate links in the production process is specifically as follows:

[0066] If the cumulative difference analysis result determines that the beverage state at the current stage is a normal production state, the current production conditions are maintained to produce the beverage;

[0067] If the cumulative difference analysis results determine that the beverage status at the current stage is an abnormal production status, the current production link is dynamically adjusted by adjusting the power of the production machine, as follows:

[0068]

[0069] in, Indicates the production machine power before adjustment, represents the adjusted production machine power, represents the adjustment coefficient of the intermediate link, Represents the difference result of the eigenvalues ​​at two consecutive time points.

[0070] Beneficial effects of the present invention:

[0071] By dynamically selecting light sources of different wavelengths and a multi-level monitoring mechanism, accurate monitoring of each link in the beverage production process is achieved, especially real-time analysis of absorbance and transmittance, which improves the accuracy of production line status monitoring;

[0072] A dynamic monitoring model combining time series difference and time-space difference analysis was constructed to detect potential anomalies in the production process in a timely manner and avoid product quality problems caused by untimely detection;

[0073] Through real-time monitoring result input, production parameters can be adjusted dynamically, from basic adjustment in early production to power adjustment in the middle link, and then to quality optimization in the finished product link, realizing a dynamic adjustment mechanism of closed-loop control, ensuring production stability and product consistency;

[0074] By extracting features in terms of time series and wavelength, and dynamically constructing feature vectors, it is possible to capture subtle state changes in the beverage production process, providing a reliable data basis for subsequent state judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0076] Figure 1 The present invention is a schematic diagram of the overall system composition structure of a condition monitoring and early warning system for a Sargassum fusiformis beverage production line. DETAILED DESCRIPTION

[0077] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0078] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0079] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0080] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0081] Meanwhile, in the description of the present invention, it should be noted that the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0082] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0083] Example 1

[0084] Reference Figure 1 , is an embodiment of the present invention, which provides a status monitoring and early warning system for a sea lettuce beverage production line, including a data acquisition and processing module for collecting data from various links of the beverage production line; a status monitoring module for monitoring and early warning the beverage status based on the collected production line data; and a feedback adjustment module for dynamically adjusting the production line according to the monitored beverage status to achieve normal production of the sea lettuce beverage.

[0085] Specifically, the data acquisition and processing module is used to collect data from various links of the beverage production line, including a data acquisition unit and a data processing unit. The data acquisition unit uses sensors to collect production data from various links of the production line, and the data processing unit processes the data collected by the sensors for subsequent status monitoring and early warning.

[0086] Furthermore, the data acquisition unit collects production data of each link of the beverage production line using an optical sensor. The optical sensor dynamically selects light sources of different wavelengths to illuminate the beverage sample, receives optical data passing through the beverage sample, and converts the received optical data into a spectral signal, as follows:

[0087] Using light sources of different wavelengths to illuminate beverage samples in the production line, the dynamic selection of wavelengths is based on the dynamic selection of different production links of beverages.

[0088] When the beverage production link is the first production stage, the length is selected A light source with a wavelength of irradiates the beverage sample;

[0089] When the beverage production link is the first production stage, the length is selected A light source with a wavelength of irradiates the beverage sample;

[0090] When the beverage production link is the third production stage, the length is selected A light source with a wavelength of irradiates the beverage sample;

[0091] According to different wavelengths, data from different beverage production links are collected, including the concentration of beverage samples and the transmittance of beverage samples.

[0092] The concentration of the beverage sample,

[0093]

[0094] The light transmittance of the beverage sample,

[0095]

[0096] in, Indicated in wavelength The absorbance at this wavelength is the absorption intensity of the beverage sample. Indicated in wavelength The molar absorption coefficient under the current wavelength is the absorption capacity of light. represents the distance traveled by light in the beverage sample, Indicated in wavelength The concentration of the beverage sample was Indicated in wavelength The light transmittance of the beverage samples below.

[0097] It should be noted that the concentration of the collected beverage samples and the light transmittance of the beverage samples , sorted according to the collected time series and constructed into the corresponding data set, then,

[0098] Concentration data set of beverage samples , ,in, Indicated in wavelength Next The concentration of the beverage sample at each moment, Indicated in wavelength Next The concentration of the beverage sample at each moment;

[0099] Transmittance collection of beverage samples , ,in, Indicated in wavelength Next The light transmittance of the beverage sample at each moment, Indicated in wavelength Next The light transmittance of the beverage sample at each moment.

[0100] Furthermore, the data processing unit processes the collected beverage sample concentration data set and beverage sample transmittance data set, and sends the processed data to the status monitoring module for production status monitoring of beverage production, including data preprocessing and data feature extraction.

[0101] Specifically, the data preprocessing is to eliminate the noise in the data set through smoothing filtering to make the data smoother, and normalize the data after smoothing filtering to the same scale for subsequent feature extraction and comparison. Smoothing and normalization processing are both existing public technologies that can be easily associated with those skilled in the art and will not be described in detail in this embodiment.

[0102] The data feature extraction is to extract key feature points from the concentration data set and the transmittance data set, and use them for subsequent monitoring of the beverage production status. The specific feature extraction is as follows:

[0103] The collected concentration data set and transmittance data set are constructed into a multi-dimensional data matrix, then,

[0104]

[0105] in, The wavelength is , No. At the moment, the concentration data and transmittance data corresponding to the beverage sample, as well as They represent the wavelength type, acquisition time, and index of the beverage sample respectively;

[0106] According to the constructed multi-dimensional data matrix, data feature extraction is completed, then,

[0107] Based on the absorption peaks under time fluctuations, features are extracted to capture the microscopic changes of beverages during the production process, specifically:

[0108]

[0109] in, Indicates wavelength The rate of change of absorbance with time under , Respectively represent Time and Data collected at all times;

[0110] Based on the transmittance under time and wavelength, features are extracted to capture the volatility and trend changes in the beverage production process, specifically:

[0111]

[0112] in, Indicates wavelength The average light transmittance at different times under Indicates wavelength At the moment Light transmittance;

[0113] The extracted features are constructed as new feature vectors, then we have:

[0114]

[0115] in, represents the constructed feature vector, Indicates wavelength The rate of change of absorbance with time under Indicates wavelength The average light transmittance at different times.

[0116] It should be noted that the constructed feature vector is sent to the status monitoring module, and the status monitoring module completes the monitoring and early warning of the beverage production status by performing feature analysis on the feature values ​​in the feature vector.

[0117] Furthermore, the status monitoring module monitors and warns the beverage status based on the collected production line data. It does so by building a multi-level monitoring mechanism to accurately monitor the status of each link in the beverage production process. The multi-level monitoring mechanism relies on eigenvalue analysis at different levels. Each level monitors parameters at different production stages and decomposes the eigenvectors into subsets of different dimensions. Each level is independently monitored and analyzed, and data input is provided to the next level based on the output results of each level. The status of the beverage production link is monitored layer by layer, including hierarchical feature monitoring and hierarchical output transmission.

[0118] Specifically, the hierarchical feature monitoring is to monitor the beverage production process based on specific feature values ​​at different levels, as follows:

[0119] The first layer monitors the earliest production stage of beverages through the basic eigenvalues ​​in the eigenvector, then:

[0120] According to the absorbance gradient in the eigenvector And the average transmittance Detect the changing trends in the earliest stages of beverage production, specifically:

[0121] Setting the absorbance gradient benchmark threshold for the earliest production stage of beverages And the transmittance mean threshold ,

[0122] If the absorbance gradient and transmittance mean in the feature vector are compared with the set threshold and satisfy the formula When , it means that the earliest production link of the beverage is abnormal;

[0123] The second layer receives the time series data of the absorbance gradient and the mean transmittance from the first layer, and monitors through time series difference analysis to identify the continuous changes of beverage ingredients during the production process, specifically:

[0124] By taking the difference between the eigenvectors of two consecutive time points and calculating the change of the two eigenvalues ​​over time, we have:

[0125]

[0126] in, express The eigenvalues ​​in the eigenvector at time instant, express The eigenvalues ​​in the eigenvector at time instant, Represents the difference result of the eigenvalues ​​at two consecutive time points;

[0127] The rate of change of the eigenvalue is calculated based on the difference of the eigenvalues ​​of two consecutive time points, then,

[0128] Cumulative difference analysis is performed based on a moving window, specifically:

[0129]

[0130] in, Indicates the size of the moving window, which is the length of two time periods and is set by the implementer according to the actual application scenario. Indicates the past The cumulative difference of all eigenvalues ​​at each time point, Indicates The characteristic value is The difference result of time;

[0131] According to the set standard threshold Determine whether the current stage of beverage production has experienced abnormal state changes, if yes,

[0132] If the cumulative difference result meets the standard threshold, the formula , it means that there is no abnormality in the beverage production status at the current stage;

[0133] If the cumulative difference result meets the standard threshold, the formula , it means that the beverage production status at the current stage is abnormal;

[0134] The third layer is to deeply monitor the turbidity of the finished beverage and identify the status of the finished beverage by receiving the spatiotemporal differential analysis results and the cumulative differential analysis results from the second layer, as follows:

[0135] For beverages in normal production status, in-depth monitoring of the turbidity of the finished beverage is carried out.

[0136] Set a specific wavelength The light source is used to irradiate the standard beverage product to obtain the absorbance of the standard beverage product. And light transmittance , and construct the linear relationship between the turbidity of the standard beverage product and the absorbance and transmittance, then,

[0137]

[0138] in, Indicates the turbidity of standard beverage products. Indicates a specific wavelength The absorbance of the finished standard beverage is as follows: Indicates a specific wavelength The light transmittance of the finished standard beverage is as follows: , They represent the slope and intercept of the linear relationship function respectively, which are affected by the wavelength of the light source and are determined by the implementer according to the actual application scenario;

[0139] According to the constructed linear relationship function, and set a certain slope and intercept, use the light source with the same wavelength and angle to illuminate the finished beverage, and record the absorbance and transmittance of each finished beverage under the light source, and use the result as the input of the linear relationship function. Finally, monitor the status of the finished beverage according to the output of the function, then,

[0140] Absorbance input for linear relationship function And the transmittance input , the output is:

[0141]

[0142] At the same time, the average turbidity of the finished beverage in the current batch is calculated, then,

[0143]

[0144] in, Indicates a specific wavelength The absorbance of the current finished beverage is Indicates a specific wavelength The light transmittance of the current finished beverage is , They represent the slope and intercept of the linear relationship function respectively. They are affected by the wavelength of the light source and are determined by the implementer according to the actual application scenario. Indicates the turbidity of the current finished beverage, which is used to determine whether the current finished beverage is qualified. Indicates the average turbidity of the current batch of finished beverages, which is used to determine whether the current finished beverages are qualified. Specifically:

[0145] When the calculated turbidity of the current beverage product is compared with the turbidity of the standard beverage product, the formula is satisfied. When , it indicates that the current beverage product is qualified;

[0146] When the calculated turbidity of the current beverage product is compared with the turbidity of the standard beverage product, the formula is satisfied. , it means that the current finished beverage is unqualified.

[0147] The hierarchical output transmission is based on the monitoring of different levels relying on the output results of the previous level, and constructs a hierarchical progressive monitoring mechanism, as follows:

[0148] The first layer monitors the earliest stage of beverage production based on the absorbance gradient and the average transmittance, and determines whether the current beverage production status is normal. Finally, the judgment result, absorbance gradient and average transmittance are transmitted to the second layer.

[0149] The second layer uses spatiotemporal differential analysis based on the data transmitted by the first layer to monitor the changing trend and status of the intermediate links of beverage production, and transmits the monitored status to the third layer;

[0150] The third layer constructs a turbidity linear relationship function based on the status of the intermediate links of beverage production transmitted by the second layer and uses standard beverage products, and judges the status of the beverage product based on the output result of the relationship function.

[0151] Furthermore, the feedback adjustment module formulates a dynamic feedback adjustment mechanism based on the output results of the three-layer monitoring mechanism of the beverage production status, and dynamically adjusts the production status in real time to ensure that the parameters in the production process remain within the optimal range, thereby ensuring the quality of the finished product.

[0152] Specifically, the dynamic feedback adjustment mechanism is a closed-loop control mechanism, in which the input is the status information from the three-layer monitoring mechanism, and the output is the dynamic adjustment of production parameters, including basic adjustment of early production links, adjustment of intermediate links in the production process, and quality adjustment of final products.

[0153] Furthermore, the basic adjustment of the early production link is based on the absorbance gradient, transmittance mean and the earliest production link status judgment result of the first-level monitoring mechanism, and is specifically:

[0154] If the earliest monitored production link status is normal production, no adjustment is required and the current production conditions can be maintained for beverage production;

[0155] If the earliest production link status monitored is abnormal, dynamic basic adjustment is performed according to the monitoring results of absorbance gradient and transmittance average, then,

[0156] When the earliest production link is abnormal, the raw materials of the beverage are not mixed evenly, resulting in an increase in the absorbance gradient of the beverage sample and a decrease in transmittance. At this time, dynamic basic adjustment is achieved by extending the raw material mixing time, specifically:

[0157]

[0158] in, Indicates the mixing time of raw materials before adjustment, Indicates the adjusted raw material mixing time, Indicates the basic adjustment coefficient, which is set by the implementer according to the actual application scenario. represents the absorbance gradient, Indicates the absorbance gradient benchmark threshold of the earliest production stage of the beverage.

[0159] The adjustment of the intermediate links in the production process is carried out according to the spatiotemporal differential analysis results of the second-layer monitoring mechanism, specifically:

[0160] If the cumulative difference analysis result determines that the beverage state at the current stage is a normal production state, no adjustment is required, and the current production conditions are maintained for beverage production;

[0161] If the cumulative difference analysis result determines that the beverage status at the current stage is an abnormal production status, the machine power in the beverage production link is abnormal, resulting in an abnormal cumulative difference analysis result. At this time, the dynamic adjustment of the current production link is achieved by adjusting the production machine power, as follows:

[0162]

[0163] in, Indicates the production machine power before adjustment, represents the adjusted production machine power, It indicates the adjustment coefficient of the intermediate link, which is set by the implementer according to the actual application scenario. Represents the difference result of the eigenvalues ​​at two consecutive time points.

[0164] The quality adjustment of the final product is to adjust the quality of the final product according to the turbidity of the beverage product and the state judgment result of the beverage product of the third-level monitoring mechanism, as follows:

[0165] If the monitored turbidity determines that the state of the current finished beverage is qualified, no adjustment is required and the current production conditions are maintained for beverage production;

[0166] If the monitored turbidity determines that the current finished beverage is unqualified, the turbidity of the finished beverage before packaging exceeds the standard, resulting in abnormal turbidity. At this time, the quality of the final product can be adjusted by adjusting the filtration time before packaging the beverage, as follows:

[0167]

[0168] in, Indicates the filtering time before adjustment, Indicates the adjusted filtering time, It indicates the quality adjustment coefficient of the final product, which is set by the implementer according to the actual application scenario. Indicates the turbidity of standard beverage products. Indicates the turbidity of the current finished beverage.

[0169] It should be noted that the dynamic feedback regulation mechanism is based on the output results of the three-layer monitoring mechanism, and dynamically adjusts the production parameters in a hierarchical manner to ensure that each link in the beverage production process is in the best state. The dynamic regulation mechanism optimizes the entire beverage production process through closed-loop control and combines spatiotemporal differential analysis with feedback regulation to ensure the stable quality of the finished product.

[0170] Furthermore, if the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the system described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0171] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0172] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0173] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment may not be described (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those features that are not relevant to implementing the invention).

[0174] It will be appreciated that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will be a routine task of design, fabrication, and production for those of ordinary skill having the benefit of this disclosure without undue experimentation.

[0175] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A status monitoring and early warning system for a Sargassum fusiformis beverage production line, characterized in that: include, A data acquisition and processing module collects optical data of beverage samples by dynamically selecting light sources of different wavelengths and constructs a data set, constructs a multi-dimensional feature matrix based on the data set and performs feature extraction to construct the extracted features into a new feature vector; And, the status monitoring module monitors the status of each link of beverage production by building a multi-level monitoring mechanism, specifically: The first layer monitors the beverage at the earliest production stage through the basic eigenvalues ​​in the eigenvector; The second layer receives the time series data of the absorbance gradient and the mean transmittance from the first layer, and monitors it through time series difference analysis to identify the continuous changes of beverage ingredients during the production process; The third layer receives the spatiotemporal differential analysis results and cumulative differential analysis results from the second layer to conduct in-depth monitoring of the turbidity of the finished beverage and identify the status of the finished beverage. The output results of each layer provide data input for the next layer, and monitor the status of the beverage production link layer by layer. Also includes: The feedback adjustment module is used to take the judgment information on the beverage production status from the three-layer monitoring mechanism as input, and the dynamic adjustment results of the production parameters as output, to perform basic adjustments to the early production links, adjustments to the intermediate links in the production process, and dynamic feedback adjustments to the quality of the final product.

2. A state monitoring and early warning system for a Sargassum beverage production line as claimed in claim 1, characterized in that: The optical data of the beverage sample collected by dynamically selecting light sources of different wavelengths is specifically as follows: When the beverage production link is the first production stage, the length is selected A light source with a wavelength of irradiates the beverage sample; When the beverage production stage is the second production stage, the length is selected A light source with a wavelength of irradiates the beverage sample; When the beverage production link is the third production stage, the length is selected A light source with a wavelength of irradiates the beverage sample; According to different wavelengths, data from different beverage production links are collected, including the concentration of beverage samples and the transmittance of beverage samples. The concentration of the beverage sample, The light transmittance of the beverage sample, in, Indicated in wavelength The absorbance under Indicated in wavelength The molar absorption coefficient under represents the distance traveled by light in the beverage sample, Indicated in wavelength The concentration of the beverage sample was Indicated in wavelength The light transmittance of the beverage samples below.

3. A state monitoring and early warning system for a Sargassum beverage production line as claimed in claim 2, characterized in that: The feature extraction of the multi-dimensional feature matrix is ​​as follows: in, Indicates wavelength The rate of change of absorbance with time under , Respectively represent Time and Data collected at all times; in, Indicates wavelength The average light transmittance at different times under Indicates wavelength At the moment The light transmittance, Indicates the upper limit of the collection time period; The extracted features are constructed as new feature vectors, then we have: in, represents the constructed feature vector, Indicates wavelength The rate of change of absorbance with time under Indicates wavelength The average light transmittance at different times.

4. A state monitoring and early warning system for a Sargassum beverage production line as claimed in claim 3, characterized in that: The beverages were monitored at the earliest stages of production as follows: According to the absorbance gradient in the eigenvector And the average transmittance Detect the changing trends in the earliest stages of beverage production, specifically: Setting the absorbance gradient benchmark threshold for the earliest production stage of beverages And the transmittance mean threshold , If the absorbance gradient and transmittance mean in the feature vector are compared with the set threshold and satisfy the formula When it is on, it means that the status of the earliest production link of the beverage is abnormal.

5. The state monitoring and early warning system for a Sargassum fusiformis beverage production line according to claim 4, characterized in that: The identification of the continuous changes in beverage ingredients during the production process is as follows: in, express The eigenvalues ​​in the eigenvector at time instant, express The eigenvalues ​​in the eigenvector at time instant, Represents the difference result of the eigenvalues ​​at two consecutive time points; Cumulative difference analysis is performed based on a moving window, specifically: in, Indicates the size of the moving window, which is the length of two time periods and is set by the implementer according to the actual application scenario. Indicates the past The cumulative difference of all eigenvalues ​​at each time point, Indicates The characteristic value is The difference result at time, represents the total number of eigenvalues ​​in the eigenvector, Indicates the upper limit of the time period for cumulative analysis; According to the set standard threshold Determine whether the current stage of beverage production has experienced abnormal state changes, if yes, If the cumulative difference result meets the standard threshold, the formula , it means that there is no abnormality in the beverage production status at the current stage; If the cumulative difference result meets the standard threshold, the formula , it means that the beverage production status at the current stage is abnormal.

6. A state monitoring and early warning system for a Sargassum beverage production line as claimed in claim 5, characterized in that: The state of identifying the finished beverage product is specifically as follows: For beverages in normal production status, in-depth monitoring of the turbidity of the finished beverage is carried out. Set a specific wavelength The light source is used to irradiate the standard beverage product to obtain the absorbance of the standard beverage product. And light transmittance , and construct the linear relationship between the turbidity of the standard beverage product and the absorbance and transmittance, then, in, Indicates the turbidity of standard beverage products. Indicates a specific wavelength The absorbance of the finished standard beverage is as follows: Indicates a specific wavelength The light transmittance of the finished standard beverage is as follows: , Respectively represent the slope and intercept in the linear relationship function; According to the constructed linear relationship function, and set a certain slope and intercept, use the light source with the same wavelength and angle to illuminate the finished beverage, and record the absorbance and transmittance of each finished beverage under the light source, and use the result as the input of the linear relationship function. Finally, monitor the status of the finished beverage according to the output of the function, then, Absorbance input for linear relationship function And the transmittance input , the output is: At the same time, the average turbidity of the finished beverage in the current batch is calculated, then, in, Indicates a specific wavelength The absorbance of the current finished beverage is Indicates a specific wavelength The light transmittance of the current finished beverage is , They represent the slope and intercept of the linear relationship function respectively. Indicates the total number of beverage products involved in monitoring in the current batch. Indicates the turbidity of the current finished beverage. Indicates The turbidity of the finished beverage, Indicates the average turbidity of the current batch of finished beverages, which is used to determine whether the current finished beverages are qualified. Specifically: When the calculated turbidity of the current beverage product is compared with the turbidity of the standard beverage product, the formula is satisfied. When , it indicates that the current beverage product is qualified; When the calculated turbidity of the current beverage product is compared with the turbidity of the standard beverage product, the formula is satisfied. , it means that the current finished beverage is unqualified.

7. A state monitoring and early warning system for a Sargassum fusiformis beverage production line as claimed in claim 6, characterized in that: The basic adjustments to the early production stages are as follows: If the earliest monitored production link status is normal production, maintain the current production conditions to produce beverages; If the earliest production link status monitored is abnormal, dynamic basic adjustment is performed according to the monitoring results of absorbance gradient and transmittance average, then, When the earliest production link is abnormal, dynamic basic adjustment is achieved by extending the raw material mixing time, specifically: in, Indicates the mixing time of raw materials before adjustment, Indicates the adjusted raw material mixing time, represents the basic adjustment coefficient, represents the absorbance gradient, Indicates the absorbance gradient benchmark threshold of the earliest production stage of the beverage.

8. The state monitoring and early warning system for a Sargassum fusiformis beverage production line according to claim 7, characterized in that: The adjustment of the intermediate links in the production process is specifically as follows: If the cumulative difference analysis result determines that the beverage state at the current stage is a normal production state, the current production conditions are maintained to produce the beverage; If the cumulative difference analysis results determine that the beverage status at the current stage is an abnormal production status, the current production link is dynamically adjusted by adjusting the power of the production machine, as follows: in, Indicates the production machine power before adjustment, represents the adjusted production machine power, represents the adjustment coefficient of the intermediate link, Represents the difference result of the eigenvalues ​​at two consecutive time points.

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