A Chinese herbal medicine drying monitoring system based on the Internet of Things

Abstract

The present invention relates to the field of Internet of Things monitoring technology, specifically a Chinese herbal medicine drying monitoring system based on the Internet of Things, the system includes a gas component monitoring module, a density measurement and analysis module, a data anomaly correction module, a drying endpoint determination module, and a terminal data storage module. In the present invention, by acquiring gas concentration data within a fixed time window and analyzing the changing trend of the gas component ratio curve, the sensitivity to changes in the gas environment is improved, the density measurement adopts a multi-monitoring point method, the density change rate is calculated, and the gas component data is combined to form a multi-dimensional drying state analysis, reduce the impact of local errors on the overall judgment, detect abnormal fluctuations through trend consistency calculation, correct mutation data, prevent sudden measurement errors from interfering with the drying state determination, combine the gas component ratio change trend and the density change trend, adjust the drying endpoint determination basis, adapt to the drying characteristics of different batches of Chinese herbal medicines, and improve the accuracy of the drying endpoint determination.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things monitoring, and in particular to a Chinese medicinal material drying monitoring system based on the Internet of Things. Background Art

[0002] The field of Internet of Things monitoring technology includes remote monitoring and control systems based on sensors, communication networks and data processing technologies. The core content is to obtain the status information of the target object through distributed sensing devices, and transmit it to the data processing center for analysis through wireless or wired communication methods to achieve real-time monitoring and control of the target object. The overall technical field includes sensor technology, data transmission technology, remote access technology and intelligent analysis technology. Among them, sensor technology is used to collect environmental parameters such as temperature and humidity, air pressure, light intensity, etc.; data transmission technology relies on wireless communication protocols or wired connections to upload and send data; remote access technology uses the Internet or local area network to support users to remotely obtain monitoring data; data processing is completed through preset calculation methods, providing visual information or executing automatic control commands.

[0003] Among them, the TCM drying monitoring system based on the Internet of Things refers to a system that uses distributed sensing equipment to collect environmental parameters during the drying process and transmits data to the monitoring terminal for analysis through a wireless network. In view of the monitoring needs of key parameters such as temperature and humidity, air flow velocity, and material moisture content during the drying process of TCM, a temperature and humidity sensor is used to measure the real-time temperature and humidity values ​​of the drying environment, a wind speed sensor is used to obtain air circulation conditions, and a near-infrared moisture measurement device is used to detect the moisture content of TCM. The acquired data is transmitted to the monitoring terminal through a wireless communication protocol, and the terminal device performs data screening based on the set critical value, and performs parameter optimization calculations in combination with historical drying data to achieve dynamic adjustment of the drying process.

[0004] The existing monitoring process of drying Chinese herbal medicines relies on single parameter monitoring, and the gas composition analysis is limited to the changes in the concentration of a single gas. There is a lack of correlation analysis between gas components, which makes it difficult to fully reflect the microscopic state of the drying environment. The density measurement method is mainly based on single point or limited measurement points, which is greatly affected by local state fluctuations and is easily affected by the deviation of individual monitoring points. The reliability of the overall data is affected. Data processing lacks mutation detection and error correction mechanisms. Sudden fluctuations in the drying process may lead to misjudgment and affect the real-time assessment of the drying state. The determination of the drying endpoint relies on a fixed time or a single parameter threshold, and lacks dynamic trend analysis, resulting in insufficient adaptability to different varieties and batches, which may cause problems of insufficient or excessive drying, affecting the stability of product quality. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a traditional Chinese medicine drying monitoring system based on the Internet of Things.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A Chinese herbal medicine drying monitoring system based on the Internet of Things comprises: The gas component monitoring module obtains the gas concentration data within a fixed time window, calculates the ratio of oxygen to carbon dioxide, the ratio of oxygen to water vapor, the ratio of carbon dioxide to water vapor, and the current gas component ratio deviation value, determines the trend of the gas component ratio curve, and generates the gas ratio deviation trend analysis results; The density measurement and analysis module obtains the density measurement data of Chinese medicinal materials at each monitoring point in the drying bin at fixed time intervals, calculates the density change rate at the current time point, and generates density change trend analysis results; The data anomaly correction module detects whether there is a sudden change in the density change rate at adjacent time points based on the density change trend analysis result, removes error data, and obtains updated density change trend data; The drying endpoint determination module matches the gas ratio deviation trend analysis result and the updated density change trend data with the preset standard to obtain the determination result of the completion of drying of the Chinese medicinal materials; The terminal data storage module stores the density of Chinese medicinal materials, the proportion of gas components, and the temperature and humidity data of the drying chamber based on the determination result of the completion of the drying of Chinese medicinal materials, and generates batch drying record data of Chinese medicinal materials.

[0007] As a further solution of the present invention, the gas proportion deviation trend analysis results include gas component proportion deviation values, gas component change trends, and gas component ratio time series data; the density change trend analysis results include density change rate and density change trend data; the updated density change trend data include density mutation points, trend consistency analysis results, and density data after error elimination; the Chinese medicinal materials drying completion judgment results include density change rate and change threshold comparison results, gas component proportion change stability analysis results, and drying endpoint judgment status records; the Chinese medicinal materials batch drying record data include final density data, gas component proportion data, and temperature and humidity data inside the drying bin.

[0008] As a further solution of the present invention, the gas component monitoring module includes: The gas concentration extraction submodule obtains the gas component concentration data within a fixed time window of the drying chamber through the gas sensor, including oxygen concentration, carbon dioxide concentration, and water vapor concentration, and stores the gas concentration data at different time points to obtain gas concentration time series data; The gas ratio calculation submodule is based on the gas concentration time series data and adopts the formula: ; ; ; Calculate the ratio of oxygen to carbon dioxide respectively , oxygen to water vapor ratio , carbon dioxide to water vapor ratio , and obtain the gas ratio time series data, where represents the oxygen concentration, represents the carbon dioxide concentration, represents the water vapor concentration; The ratio reference curve construction submodule selects the ratio data of the initial drying stage based on the gas ratio time series data, calculates the ratio change rate at the difference time point, and fits the ratio change trend to establish the gas component ratio reference curve; The deviation trend analysis submodule calculates the deviation value of the current gas ratio data according to the gas component ratio reference curve, performs time series analysis on the deviation value, determines the gas ratio change trend, and obtains the gas ratio deviation trend analysis result.

[0009] As a further solution of the present invention, the density measurement and analysis module includes: The density data acquisition submodule embeds a high-frequency electromagnetic wave sensor inside the drying bin to obtain the density measurement data of the Chinese medicinal materials at each monitoring point in the drying bin within a fixed time interval, and stores the density time series data; The density change calculation submodule is based on the density time series data and adopts the formula: ; Calculate the density change rate at the current time point , get the density change rate data, Represents the density at the current time point, represents the density at the previous time point, represents the time interval between adjacent time windows; The density change trend analysis submodule calculates the density change trend according to the density change rate data, analyzes the fluctuation of the change rate in the time series, and obtains the density change trend analysis result.

[0010] As a further solution of the present invention, the data anomaly correction module includes: The density mutation detection submodule calculates the difference of density change rate at adjacent time points based on the density change trend analysis result and the density change trend data of multiple time windows, and determines whether there is a mutation point, and obtains density mutation point data; The trend consistency calculation submodule uses the formula according to the density mutation point data: ; Calculate the trend consistency of adjacent time points , get the trend consistency data, where Represents the density change rate at the current time point, Represents the rate of change of density at the previous time point; The error data elimination submodule eliminates data points whose trend consistency is lower than the set threshold according to the trend consistency data, updates the density change trend, and obtains the updated density change trend data. As a further solution of the present invention, the drying endpoint determination module includes: The density change determination submodule calculates the density change rate at the current time point based on the updated density change trend data, and determines whether it is lower than a preset change threshold, and obtains a density change rate determination result; The gas ratio stability calculation submodule analyzes the fluctuation degree of the gas component ratio change trend based on the gas ratio deviation trend analysis result, using the formula: ; Calculate the gas ratio change trend stability score at the current time point , and obtain the gas ratio stability data, where Representative The gas ratio at the time, Representative The gas ratio at the time, is the number of time steps within the calculation window; The drying completion determination submodule determines whether the density change rate is lower than the change threshold and whether the gas ratio change trend is stable based on the density change rate determination result and the gas ratio stability data. If both meet the preset standards, the drying completion determination result of the Chinese medicinal materials is obtained.

[0011] As a further solution of the present invention, the terminal data storage module includes: The density data recording submodule counts the final density data of the current batch of Chinese medicinal materials based on the determination result of the completion of drying of the Chinese medicinal materials, stores the data, and obtains the final density data of the batch; The gas ratio storage submodule counts the current batch gas component ratio data based on the batch final density data, and obtains the batch gas ratio and environmental data in combination with the temperature and humidity data inside the drying chamber; The batch information archiving submodule records the drying endpoint status information of the current batch according to the batch gas ratio and environmental data, organizes and stores the data, and obtains the batch drying record data of Chinese medicinal materials.

[0012] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by acquiring gas concentration data within a fixed time window, calculating the ratio of oxygen, carbon dioxide and water vapor, and analyzing the changing trend of the gas component ratio curve, the gas composition monitoring is extended from a single concentration measurement to a ratio analysis, and the sensitivity to the change of the gas environment is improved. The density measurement adopts a multi-monitoring point method to calculate the density change rate, and combines the gas component data to form a multi-dimensional drying state analysis. Compared with the single-point measurement method, the influence of local errors on the overall judgment is reduced. The error elimination mechanism detects abnormal fluctuations through trend consistency calculation, corrects mutation data, improves the stability of the measurement data, and prevents sudden measurement errors from interfering with the drying state judgment. Combined with the changing trend of the gas component ratio and the density changing trend, the drying endpoint judgment basis is adjusted, and the endpoint judgment is turned from static time setting to dynamic trend analysis, which adapts to the changes in the drying characteristics of different batches of Chinese medicinal materials and improves the accuracy of the drying endpoint judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flow chart of the gas component monitoring module of the present invention; Figure 3 This is a flow chart of the density measurement and analysis module of the present invention; Figure 4 This is a flow chart of the data anomaly correction module of the present invention; Figure 5 This is a flow chart of the drying endpoint determination module of the present invention; Figure 6 This is a flow chart of the terminal data storage module of the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0015] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0016] See also Figure 1, a Chinese medicinal material drying monitoring system based on the Internet of Things includes: The gas component monitoring module obtains the gas component concentration data within a fixed time window through the gas sensor inside the drying chamber, extracts the oxygen concentration, carbon dioxide concentration, and water vapor concentration, calculates the ratio of oxygen to carbon dioxide, oxygen to water vapor, and carbon dioxide to water vapor, stores the ratio time series data, draws the gas component ratio reference curve in the initial drying stage of Chinese medicinal materials, and calculates the current gas component ratio deviation value. When the deviation value exceeds the set deviation threshold, it determines the change trend of the gas component ratio curve and generates the gas ratio deviation trend analysis result; The density measurement and analysis module obtains the density measurement data of Chinese medicinal materials at each monitoring point in the drying bin at fixed time intervals through the embedded high-frequency electromagnetic wave sensor in the drying bin, calculates the density change rate at the current time point, and generates density change trend analysis results; The data anomaly correction module is based on the density change trend analysis results and combines the density change trend data of multiple time windows to detect whether there is a sudden change in the density change rate at adjacent time points, determine the mutation point of the density of Chinese medicinal materials, and calculate the trend consistency of adjacent time points, eliminate error data, and obtain updated density change trend data; The drying endpoint determination module determines whether the density change rate is lower than the change threshold based on the gas ratio deviation trend analysis results and the updated density change trend data, and calculates whether the gas component ratio change trend is stable. If the density change rate and the gas component ratio change trend meet the preset standards, the drying completion determination result of the Chinese medicinal materials is obtained; The terminal data storage module is based on the determination result of the completion of drying of Chinese medicinal materials, and counts the final density data and gas component ratio data of the current batch of Chinese medicinal materials. It combines the temperature and humidity data inside the drying chamber, records the drying endpoint status information of the current batch, and generates batch drying record data of Chinese medicinal materials.

[0017] The results of the gas proportion deviation trend analysis include the gas component proportion deviation value, gas component change trend, and gas component ratio time series data; the results of the density change trend analysis include the density change rate and density change trend data; the updated density change trend data include the density mutation point, trend consistency analysis results, and density data after error elimination; the results of the determination of the completion of Chinese medicinal materials drying include the comparison results of the density change rate and the change threshold, the gas component proportion change stability analysis results, and the drying endpoint determination status record; the Chinese medicinal materials batch drying record data include the final density data, gas component proportion data, and the temperature and humidity data inside the drying bin.

[0018] See also Figure 2 , the gas composition monitoring module includes: The gas concentration extraction submodule obtains the gas component concentration data within a fixed time window of the drying chamber through the gas sensor, including oxygen concentration, carbon dioxide concentration, and water vapor concentration, and stores the gas concentration data at different time points to obtain gas concentration time series data; Obtain gas component concentration data within a fixed time window, that is, within the set time interval, record the concentrations of oxygen, carbon dioxide, and water vapor through the gas sensor inside the drying chamber, set the data sampling time window to 10 seconds, and the acquisition frequency to 1Hz, so that 10 sets of data are obtained in each time window. Different sensors have different measurement accuracy. The measurement range of the oxygen concentration sensor is 0-25% and the accuracy is ±0.1%; the measurement range of the carbon dioxide concentration sensor is 0-5% and the accuracy is ±0.05%; the measurement range of the water vapor concentration sensor is 0-100% and the accuracy is ±0.5%. For example, within a certain time window, the average oxygen concentration collected by the sensor is 20.8%, the average carbon dioxide concentration is 0.04%, and the average water vapor concentration is 2.3%. Store these data in the database and record the timestamp of data acquisition. At different time points, the sensor will continuously collect and store data. For example, the oxygen concentration is 20.8% at t=0s, 20.79% at t=10s, 20.78% at t=20s, and so on, to construct a complete gas concentration time series data, as shown in Table 1.1.

[0019] Table 1.1 Gas concentration time series table: ; As shown in Table 1.1, at different time points, the concentrations of oxygen, carbon dioxide, and water vapor show dynamic changes. These data will be used to subsequently calculate the gas component ratios and ultimately obtain gas concentration time series data.

[0020] The gas ratio calculation submodule is based on the gas concentration time series data and uses the formula: ; ; ; Calculate the ratio of oxygen to carbon dioxide respectively , oxygen to water vapor ratio , carbon dioxide to water vapor ratio , and obtain the gas ratio time series data, where represents the oxygen concentration, represents the carbon dioxide concentration, represents the water vapor concentration; Based on the gas concentration time series data, the ratio of oxygen to carbon dioxide, the ratio of oxygen to water vapor, and the ratio of carbon dioxide to water vapor are calculated. Taking the data in Table 1.1 as an example, at t=0s: ; ; ; As time goes by, the ratios at different time points are calculated as shown in Table 1.2 below.

[0021] Table 1.2 Gas ratio time series table: ; As shown in Table 1.2, the ratios of various gas components change dynamically over time. These ratio data will be used to construct the baseline curve in the future, and ultimately obtain the gas ratio time series data.

[0022] The ratio reference curve construction submodule is based on the gas ratio time series data, selects the ratio data of the initial drying stage, calculates the ratio change rate at the difference time point, and fits the ratio change trend to establish the gas component ratio reference curve; Based on the gas ratio time series data, the ratio data of the initial drying stage is selected, the ratio change rate at different time points is calculated, and the ratio change trend is fitted to construct a benchmark ratio curve. First, the time interval is defined as the data of the first 5 minutes, and the ratio change rate is calculated at intervals of 10s: ; For example, the rate of change of the oxygen / carbon dioxide ratio from t=0s to t=10s: ; From t=10s to t=20s: ; After calculating the rate of change of all data points, the baseline curve is fitted and polynomial fitting is used to obtain the change trend of the ratio, as shown in Table 1.3.

[0023] Table 1.3 Gas ratio change rate table: ; As shown in Table 1.3, the ratio change rate data at different time points are used to fit the benchmark curve, and finally the gas component ratio benchmark curve is obtained.

[0024] The deviation trend analysis submodule calculates the deviation value of the current gas ratio data according to the gas component ratio reference curve, and performs time series analysis on the deviation value to determine the gas ratio change trend and obtain the gas ratio deviation trend analysis results; According to the gas component ratio reference curve, calculate the deviation value of the current gas ratio data. The calculation method is as follows: ; ; ; Taking the data of t=40s as an example, the benchmark ratio is 518.60: ; Through time series analysis, the rate of change is calculated based on the change trend of the current deviation value and the deviation value of the previous n moments, that is, the first-order difference of the ratio deviation is calculated. , taking the data of t=40s and t=30s as an example: ; If the rate of change remains negative, it indicates that the ratio deviation is decreasing, otherwise it indicates that the deviation is increasing. In addition, to determine the stability of the deviation trend, the second-order difference is calculated. If the absolute value of the second-order difference continues to decrease, it indicates that the deviation trend is stable. If the absolute value of the second-order difference fluctuates greatly, it indicates that the deviation changes dramatically, which may be affected by changes in the external environment, such as fluctuations in drying temperature and airflow rate. For example: ; The second-order difference absolute value threshold is set based on the measurement accuracy of the gas concentration change rate inside the drying chamber, the data sampling period, and the degree of environmental disturbance. The specific threshold is selected based on the measurement accuracy range of oxygen, carbon dioxide, and water vapor concentration (±0.1%, ±0.05%, ±0.5%), and is analyzed in combination with the actual gas concentration change data. Assuming that the average rate of change of the gas ratio is 0.05 / s, and the error range caused by the sensor measurement accuracy is between 0.01 and 0.015, the second-order difference threshold value should at least cover the maximum error impact range, while considering the impact of airflow fluctuations on gas components. Under experimental measurement, the threshold is set to 0.02. For example: ; ; Among them, 0.005 is the safety margin of the airflow disturbance. If the absolute value of the second-order difference is less than 0.02, it is considered that the deviation change trend is stable, otherwise it is necessary to further analyze the abnormal fluctuations of the drying chamber environmental parameters, such as temperature, humidity, airflow rate, etc. Finally, the gas ratio deviation trend analysis results are obtained by combining the change rate of the deviation value, the first-order difference and the second-order difference analysis.

[0025] See also Figure 3 , density measurement and analysis module includes: The density data acquisition submodule embeds a high-frequency electromagnetic wave sensor inside the drying bin to obtain the density measurement data of the Chinese medicinal materials at each monitoring point in the drying bin within a fixed time interval, and stores the density time series data; High-frequency electromagnetic wave sensors are embedded inside the drying bin to monitor the density of Chinese medicinal materials at each monitoring point in the bin in real time. The high-frequency electromagnetic wave sensors are installed at three different heights: upper, middle, and lower to ensure the comprehensiveness of the data. The density measurement values ​​of each monitoring point are collected at fixed time intervals and the data are stored in the database to form density time series data. In practical applications, for example, for a batch of astragalus root being dried, the sensor is set to collect density data every 30 minutes to detect changes in density during the drying process. The collected data includes timestamp, monitoring point number, and measured density value.

[0026] Table 2.1: Density data collection example: ; As shown in Table 2.1, density data at different time points can be used to calculate the density change rate and establish density time series data.

[0027] The density change calculation submodule is based on density time series data and uses the formula: ; Calculate the density change rate at the current time point , get the density change rate data, Represents the density at the current time point, represents the density at the previous time point, represents the time interval between adjacent time windows; Based on the density time series data, the density change rate of adjacent time windows was calculated to evaluate the density decrease during the drying process. The calculation method was to obtain the difference between the density value at the current time point and the density value at the previous time point and divide it by the time interval.

[0028] Taking the data in Table 2.1 as an example, calculate the density change rate of monitoring point 1 at 10:30:00: ; Calculate the density change rate of monitoring point 2 at 10:30:00: ; This calculation process can be applied to all time points to obtain density change rate data.

[0029] The density change trend analysis submodule calculates the density change trend based on the density change rate data, analyzes the fluctuation of the change rate in the time series, and obtains the density change trend analysis results; Based on the density change rate data, the density change trend is analyzed, and the change rate data at different time points are compared to determine whether the density decrease is stable. For example, in the 10:00-11:00 time period, the density change rates of monitoring point 1 are 0.00067g / cm³ / min and 0.00067g / cm³ / min, respectively, indicating that the density decrease trend is relatively uniform. The density change rates of monitoring point 2 are 0.001g / cm³ / min and 0.001g / cm³ / min, respectively, which also indicates that the density decrease rate is relatively stable. If the change rate increases in the subsequent time period, it may indicate that the drying rate is accelerated. It is necessary to further analyze environmental factors, such as temperature and humidity changes, to adjust the drying parameters and finally obtain the density change trend analysis results.

[0030] See also Figure 4 , the data anomaly correction module includes: The density mutation detection submodule is based on the density change trend analysis results, combined with the density change trend data of multiple time windows, calculates the difference in density change rate at adjacent time points, and determines whether there is a mutation point, and obtains the density mutation point data; Based on the results of density change trend analysis, the density change trend data in multiple time windows are called, the interval time of each time window is set to 5 minutes, the data of two adjacent time points are selected for analysis, the density change rate of each time point is extracted, and the rate change amplitude between adjacent time points is calculated. During the calculation process, for each time point The rate of change of density , using the density value and The difference between the two divided by the time interval To obtain the density value, the data measured by the sensor needs to be denoised to eliminate the random errors caused by environmental factors, such as air flow, measurement position deviation, etc. During the calculation process, if the density change rate at two adjacent time points changes Greater than the preset threshold , then the time point is judged as a density mutation point, and the threshold The normal density change range of Chinese medicinal materials during the drying process is set with reference to this range. This range is determined based on the dynamic change characteristics of the density of Chinese medicinal materials under different moisture contents. The experimental results show that in the initial stage when the moisture content decreases from 50% to 30%, the density change rate of Chinese medicinal materials is generally between 0.02g / cm³·min and 0.08g / cm³·min. When the moisture content further drops to below 15%, the density change rate gradually stabilizes and is maintained within 0.01g / cm³·min. Therefore, the density mutation threshold is set. g / cm³·min, ensuring accurate identification of abnormal points.

[0031] In time Density measured at g / cm³, in Density measured at g / cm³, time interval min, then the density change rate is: ; If the previous time point g / cm³·min, the rate of change is g / cm³·min, greater than the threshold , at this time, the point is determined to be a density mutation point, the time point is recorded and stored in the density mutation point data set, and finally the density mutation point data is obtained.

[0032] The trend consistency calculation submodule uses the formula based on the density mutation point data: ; Calculate the trend consistency of adjacent time points , get the trend consistency data, where Represents the density change rate at the current time point, Represents the rate of change of density at the previous time point; According to the density mutation point data, the trend consistency of adjacent time points is calculated. Trend consistency is used to measure the coherence of the density change rate, that is, whether the density change rate of adjacent time points remains stable. When calculating trend consistency, if the change range of the two is large, the trend consistency is low, if the change range is small, the trend consistency is high.

[0033] If time The rate of change of density is g / cm³·min, The rate of change of density is g / cm³·min, then: ; The range of trend consistency values ​​is to ,in Represents complete agreement, Represents complete inconsistency. Usually, the threshold is set To filter out points with low trend consistency, the threshold The setting is based on the stability analysis of the density change rate during the drying process. Experimental data show that when the relative fluctuation range of the density change rate is less than 50%, the trend consistency is usually above 0.6, and when the relative fluctuation range exceeds 60%, the trend consistency usually drops below 0.4 and the correlation with the error data increases. Therefore, the trend consistency threshold is set this time. , used to identify the stability of data.

[0034] If the trend consistency at a certain time point is lower than the threshold, the point contains abnormal data, and finally the trend consistency data is obtained.

[0035] The error data elimination submodule eliminates data points whose trend consistency is lower than the set threshold according to the trend consistency data, updates the density change trend, and obtains the updated density change trend data; According to the trend consistency data, remove the data points whose trend consistency is lower than the set threshold. The specific operation is as follows: traverse the trend consistency data set, for each time point ,like , the corresponding density data is marked as an outlier and removed from the data set. For example, a batch of data is recorded as follows: Table 3.1 Monitoring point density change trend data table: ; As shown in Table 3.1, the trend consistency at the time point 15 minutes Below the set threshold Therefore, the data at this time point is removed, and the change trend of the remaining data is recalculated. After removing the abnormal data, the density change trend is smoother, avoiding the interference of abnormal data on the overall trend, and finally obtaining the updated density change trend data.

[0036] See also Figure 5 , the drying endpoint determination module includes: The density change determination submodule calculates the density change rate at the current time point based on the updated density change trend data, and determines whether it is lower than the preset change threshold, and obtains the density change rate determination result; Based on the updated density change trend data, the density change rate at the current time point needs to be calculated to determine whether it is lower than the set change threshold. First, a set of density time series data is selected. Assume that a monitoring point in the drying warehouse is at time , , The density data at the points are 0.85g / cm³, 0.83g / cm³, and 0.82g / cm³ respectively. The density change rate at adjacent time points can be calculated. Represents the adjacent time interval (10 minutes). The calculation process is as follows: ; ; The density change threshold is set based on the fact that during the drying process of Chinese medicinal materials, the density change rate drops rapidly in the early stage and then tends to stabilize. According to the relationship between the water evaporation rate and the density change, the density change rate at different drying stages is selected, and its fluctuation in different moisture content ranges is analyzed. Combined with experimental data, when the moisture content of Chinese medicinal materials is lower than 10% (in line with the conventional drying endpoint standard), the density change rate tends to 0.001~0.002g / cm³ / min. At this time, the internal moisture diffusion and surface evaporation of the material reach a balance. Therefore, 0.002g / cm³ / min is taken as the change threshold to ensure that when the rate is lower than this value, it is judged that the drying process is close to completion.

[0037] exist At this moment, the density change rate of 0.001g / cm³ / min is lower than the threshold value, which meets the judgment condition and obtains the density change rate judgment result.

[0038] The gas ratio stability calculation submodule analyzes the fluctuation degree of the gas component ratio change trend based on the gas ratio deviation trend analysis results, using the formula: ; Calculate the gas ratio change trend stability score at the current time point , and obtain the gas ratio stability data, where Representative The gas ratio at the time, Representative The gas ratio at the time, is the number of time steps within the calculation window; Based on the gas ratio deviation trend analysis results, it is necessary to calculate the fluctuation degree of the gas component ratio change trend. First, collect a set of gas component ratio data within a time window, assuming that the oxygen to carbon dioxide ratio ( ) at time , , , The calculation windows are 1.20, 1.18, 1.17, and 1.165 respectively. ,but: ; The basis for setting the stability threshold is that the fluctuation of the gas ratio reflects the relationship between water evaporation and the change of the internal structure of Chinese medicinal materials. In the early stage of drying, due to the rapid evaporation rate of water, the gas ratio changes greatly, while near the end of drying, the evaporation of water tends to balance and the fluctuation amplitude of the gas ratio decreases. By analyzing the data of multiple batches of drying experiments, at the stage when the density change rate is close to 0.002g / cm³ / min, the fluctuation amplitude of the oxygen / carbon dioxide ratio is usually stable between 0.01 and 0.012. Therefore, 0.012 is set as the stable threshold of the gas ratio change trend. When the fluctuation degree is lower than this value, it indicates that the gas component has entered a stable state and can be used for the determination of the drying end point. 0.0117 is lower than the threshold, indicating that the change in the gas component ratio has tended to be stable, and the gas ratio stability data is obtained.

[0039] The drying completion determination submodule determines whether the density change rate is lower than the change threshold and whether the gas ratio change trend is stable based on the density change rate determination result and the gas ratio stability data. If both meet the preset standards, the drying completion determination result of the Chinese medicinal materials is obtained; The drying endpoint is determined based on the density change rate and gas ratio stability data. If the density change rate is lower than the change threshold and the gas component ratio change trend is stable, the drying completion determination conditions of the Chinese medicinal materials are met.

[0040] Combined with the above calculation results, the density change rate is The moment dropped to 0.001g / cm³ / min, which is lower than the threshold of 0.002g / cm³ / min. The trend of the gas component ratio is The calculated value at that moment is 0.0117, which is lower than the set threshold value of 0.012. Therefore, it is comprehensively judged that the drying endpoint is reached, and the result of the completion of drying of the Chinese medicinal materials is obtained.

[0041] Table 4.1 Drying endpoint determination parameters: ; As shown in Table 4.1, the density change rate dropped to less than 0.002 g / cm³ / min after 20 minutes, and the gas ratio change trend stabilized at 0.0117 at 30 minutes, which was lower than the stability threshold of 0.012, meeting the drying endpoint judgment conditions.

[0042] See also Figure 6 , the terminal data storage module includes: The density data recording submodule counts the final density data of the current batch of Chinese medicinal materials based on the determination result of the completion of drying of Chinese medicinal materials, stores it, and obtains the final density data of the batch; After the drying process of Chinese medicinal materials is completed, the accuracy of density data must be ensured in order to record the final drying state. Based on the judgment result of the completion of drying of Chinese medicinal materials, the monitoring equipment is first called to obtain the density data of all samples in the current batch, and abnormal data points are excluded to ensure data integrity. In the actual operation process, multiple high-frequency electromagnetic wave sensors are usually arranged inside the drying chamber to detect the density of Chinese medicinal materials in real time. Assuming that a batch of Chinese medicinal materials has 10 monitoring points, the density data of each monitoring point is measured as 0.85g / cm³, 0.89g / cm³, 0.87g / cm³, etc. After obtaining the density data, the data needs to be screened for abnormalities, the density mean of each monitoring point is calculated, and the density standard deviation of all monitoring points is calculated. For example, the density mean of the 10 monitoring points in this batch is 0.865g / cm³, and the standard deviation is calculated as follows: ; in, is the density value of each monitoring point, is the mean, is the number of monitoring points. The standard deviation of the batch is calculated to be 0.015g / cm³. If the standard deviation exceeds the set threshold (such as 0.02g / cm³), it is necessary to re-measure the abnormal points or eliminate the abnormal data. The final screened density data will be stored in the database and form the final density data of the batch.

[0043] The gas ratio storage submodule counts the current batch gas component ratio data based on the batch final density data, and obtains the batch gas ratio and environmental data in combination with the temperature and humidity data inside the drying chamber; To call the final density data of the batch, it is necessary to further combine the gas component ratio data inside the drying chamber to ensure the monitoring integrity of the entire drying process. The concentrations of oxygen, carbon dioxide, and water vapor are recorded at each monitoring point to calculate the gas component ratio. For example, the final gas concentration data of a batch of samples are as follows: Table 5.1 Drying end point gas component concentration data: ; As shown in Table 5.1, the gas composition data at each monitoring point fluctuates slightly, but is generally stable. The ratio of the gas components needs to be further calculated. For example, the ratio of oxygen to carbon dioxide is: ; Substitute the data from monitoring point 1 into the calculation: ; Similarly, the ratios of all monitoring points are calculated. After the calculation is completed, the standard deviation analysis is performed on the ratio data of all monitoring points, and the allowable range of ratio fluctuation is set to ensure that the ratio fluctuation is within a reasonable range. The calculation is as follows: ; in, is the gas ratio at each monitoring point, is the mean gas ratio, is the number of monitoring points. Assuming that the calculated mean of the oxygen to carbon dioxide ratio is 25.42 and the standard deviation is 0.8, the allowable fluctuation range is set as: ; The calculations show that: ; This fluctuation range is lower than the set 5% threshold, indicating that the gas composition changes stably. The 5% threshold is determined based on the gas diffusion rate and the influence of changes in ambient temperature and humidity. In practical applications, if the ambient temperature and humidity change range is controlled within 2°C, the gas component ratio change should be kept within ±5% to ensure the stability of the drying process. If it exceeds 5%, further analysis is required to determine whether there are any abnormal conditions, such as sensor errors, drying chamber airtightness problems, etc.

[0044] If all ratio fluctuations are less than 5%, the gas composition is determined to be stable, and combined with the temperature and humidity data inside the drying chamber, the batch gas ratio and environmental data are finally obtained.

[0045] The batch information archiving submodule records the drying endpoint status information of the current batch according to the batch gas ratio and environmental data, organizes and stores the data, and obtains the batch drying record data of Chinese herbal medicines; Based on the batch gas ratio and environmental data, all information needs to be archived to form complete drying endpoint status information, including density, gas component ratio and environmental parameters. When storing data, batch numbering is required for management.

[0046] For example, all data of the current batch are archived according to the batch number "2024-02-01-A". The specific storage information is as follows: Table 5.2 Drying endpoint batch data archive table: ; As shown in Table 5.2, all key data of the batch are integrated and archived. If the drying data of a batch needs to be traced later, the complete record can be queried by batch number. All data are finally stored in the database to form the batch drying record data of Chinese medicinal materials.

[0047] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A Chinese herbal medicine drying monitoring system based on the Internet of Things, characterized in that: The system comprises: The gas component monitoring module obtains the gas concentration data within a fixed time window, calculates the ratio of oxygen to carbon dioxide, the ratio of oxygen to water vapor, the ratio of carbon dioxide to water vapor, and the current gas component ratio deviation value, determines the trend of the gas component ratio curve, and generates the gas ratio deviation trend analysis results; The density measurement and analysis module obtains the density measurement data of Chinese medicinal materials at each monitoring point in the drying bin at fixed time intervals, calculates the density change rate at the current time point, and generates density change trend analysis results; The data anomaly correction module detects whether there is a sudden change in the density change rate at adjacent time points based on the density change trend analysis result, removes error data, and obtains updated density change trend data; The drying endpoint determination module matches the gas ratio deviation trend analysis result and the updated density change trend data with the preset standard to obtain the determination result of the completion of drying of the Chinese medicinal materials; The terminal data storage module stores the density of the Chinese medicinal materials, the proportion of gas components, and the temperature and humidity data of the drying chamber based on the determination result of the completion of the drying of the Chinese medicinal materials, and generates batch drying record data of the Chinese medicinal materials.

2. The Internet of Things-based Chinese herbal medicine drying monitoring system according to claim 1, characterized in that: The gas ratio deviation trend analysis results include gas component ratio deviation values, gas component change trends, and gas component ratio time series data; The density change trend analysis results include density change rate and density change trend data; the updated density change trend data include density mutation points, trend consistency analysis results, and density data after error elimination; the Chinese medicinal materials drying completion judgment results include density change rate and change threshold comparison results, gas component ratio change stability analysis results, and drying endpoint judgment status records; the Chinese medicinal materials batch drying record data include final density data, gas component ratio data, and temperature and humidity data inside the drying bin.

3. The Chinese medicinal material drying monitoring system based on the Internet of Things according to claim 1 is characterized in that: The gas component monitoring module comprises: The gas concentration extraction submodule obtains the gas component concentration data within a fixed time window of the drying chamber through the gas sensor, including oxygen concentration, carbon dioxide concentration, and water vapor concentration, and stores the gas concentration data at different time points to obtain gas concentration time series data; The gas ratio calculation submodule is based on the gas concentration time series data and adopts the formula: ； ； ； Calculate the ratio of oxygen to carbon dioxide respectively , oxygen to water vapor ratio , carbon dioxide to water vapor ratio , and obtain the gas ratio time series data, where represents the oxygen concentration, represents the carbon dioxide concentration, represents the water vapor concentration; The ratio reference curve construction submodule selects the ratio data of the initial drying stage based on the gas ratio time series data, calculates the ratio change rate at the difference time point, and fits the ratio change trend to establish the gas component ratio reference curve; The deviation trend analysis submodule calculates the deviation value of the current gas ratio data according to the gas component ratio reference curve, performs time series analysis on the deviation value, determines the gas ratio change trend, and obtains the gas ratio deviation trend analysis result.

4. The Chinese medicinal material drying monitoring system based on the Internet of Things according to claim 1 is characterized in that: The density measurement and analysis module comprises: The density data acquisition submodule embeds a high-frequency electromagnetic wave sensor inside the drying bin to obtain the density measurement data of the Chinese medicinal materials at each monitoring point in the drying bin within a fixed time interval, and stores the density time series data; The density change calculation submodule is based on the density time series data and adopts the formula: ； Calculate the density change rate at the current time point , get the density change rate data, Represents the density at the current time point, represents the density at the previous time point, represents the time interval between adjacent time windows; The density change trend analysis submodule calculates the density change trend according to the density change rate data, analyzes the fluctuation of the change rate in the time series, and obtains the density change trend analysis result.

5. The Chinese medicinal material drying monitoring system based on the Internet of Things according to claim 1 is characterized in that: The data anomaly correction module includes: The density mutation detection submodule calculates the difference of density change rate at adjacent time points based on the density change trend analysis result and the density change trend data of multiple time windows, and determines whether there is a mutation point, and obtains density mutation point data; The trend consistency calculation submodule uses the formula according to the density mutation point data: ； Calculate the trend consistency of adjacent time points , get the trend consistency data, where Represents the density change rate at the current time point, Represents the rate of change of density at the previous time point; The error data elimination submodule eliminates data points whose trend consistency is lower than a set threshold according to the trend consistency data, updates the density change trend, and obtains updated density change trend data.

6. The Chinese medicinal material drying monitoring system based on the Internet of Things according to claim 1 is characterized in that: The drying endpoint determination module comprises: The density change determination submodule calculates the density change rate at the current time point based on the updated density change trend data, and determines whether it is lower than a preset change threshold, and obtains a density change rate determination result; The gas ratio stability calculation submodule analyzes the fluctuation degree of the gas component ratio change trend based on the gas ratio deviation trend analysis result, using the formula: ； Calculate the gas ratio change trend stability score at the current time point , and obtain the gas ratio stability data, where represents the gas ratio at the first moment, Representative The gas ratio at the time, is the number of time steps within the calculation window; The drying completion determination submodule determines whether the density change rate is lower than the change threshold and whether the gas ratio change trend is stable based on the density change rate determination result and the gas ratio stability data. If both meet the preset standards, the drying completion determination result of the Chinese medicinal materials is obtained.

7. The Chinese medicinal material drying monitoring system based on the Internet of Things according to claim 1 is characterized in that: The terminal data storage module comprises: The density data recording submodule counts the final density data of the current batch of Chinese medicinal materials based on the determination result of the completion of drying of the Chinese medicinal materials, stores the data, and obtains the final density data of the batch; The gas ratio storage submodule counts the current batch gas component ratio data based on the batch final density data, and obtains the batch gas ratio and environmental data in combination with the temperature and humidity data inside the drying chamber; The batch information archiving submodule records the drying endpoint status information of the current batch according to the batch gas ratio and environmental data, organizes and stores the data, and obtains the batch drying record data of Chinese medicinal materials.

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