Intelligent control system and control method for stewed radix aconiti lateralis preparata processing

Abstract

The invention relates to the technical field of stewed radix aconiti lateralis praeparata processing, and discloses an intelligent control system and method for stewed radix aconiti lateralis praeparata processing, and the system comprises a processing monitoring module, an intelligent early warning module and a finished product detection module, the processing monitoring module is used for monitoring key parameters of the whole processing flow, and automatically adjusting the operation state of equipment; and the monitoring result and the real-time monitoring value of the key parameter are sent to the intelligent early warning module. According to the system, key parameters such as the temperature, the oxygen content, the humidity and the chaff consumption rate at each sampling moment and tail gas components are synchronously collected and analyzed in real time through the processing monitoring module, the multi-dimensional and sequential process sensing capacity is constructed, and equipment operation can be automatically adjusted when the parameters are abnormal; therefore, the process stability and the product batch consistency are greatly improved, the dependence on experience operation is reduced, the non-destructive perception judgment method of comparing the knocking voiceprint with the historical database is adopted, unqualified products can be quickly recognized and automatically removed, and the detection rate is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of processed aconite root slices, specifically to an intelligent control system and control method for processed aconite root slices. Background Technology

[0002] The core of aconite root roasting lies in the stable smoldering of rice husks. Since rice husks, as fuel, are gradually consumed over time, the processing technique for roasting aconite root slices is crucial for ensuring efficacy, safety, and stability. Traditional processing relies heavily on the experience of artisans, who subjectively determine heating temperature, time, heat, and stirring timing by observing color, smell, feel, and the sound of tapping. With the development of large-scale, industrialized production, manual experience alone is insufficient to meet the requirements of consistent quality, traceability, and environmental protection, prompting processing techniques to evolve towards automation and intelligence. However, current quality inspection methods mostly rely on manual sampling or laboratory testing, which have long testing cycles and make it difficult to remove substandard products online in real time. They also make limited use of acoustic and other sensing features, and have not formed an automatic judgment mechanism that compares with historical databases. Furthermore, current technology is insufficient to achieve refined and intelligent control and online quality judgment of the entire process of preparing aconite root slices. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides an intelligent control system and method for processing prepared aconite root slices, which has the advantages of precise control over the entire processing process and real-time removal of defective products, thus solving the aforementioned technical problems.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an intelligent control system for processing aconite root slices, comprising a processing monitoring module, an intelligent early warning module, and a finished product detection module; The processing monitoring module is used to monitor key parameters of the entire processing process. When a key parameter is abnormal, it automatically adjusts the operating status of the equipment. At the same time, it monitors the exhaust gas of the processing process and sends the monitoring results and the real-time monitoring values ​​of the key parameters to the intelligent early warning module. The intelligent early warning module is used to detect and warn of exhaust gas in the processing process. When the exhaust gas exceeds the preset exhaust gas warning value, an early warning is issued. The intelligent early warning module monitors the key parameters of the processing process in real time and calculates the deviation coefficient of any key parameter in real time. When the deviation coefficient of any key parameter exceeds the preset deviation threshold, an early warning is issued. The finished product inspection module is used to inspect the processed finished product. By comparing the processed finished product with the tapping sound prints in the historical database, the deviation coefficient of the finished product is calculated. When the deviation coefficient of the finished product exceeds the set threshold, it is judged as unqualified and the finished product is removed.

[0005] As a preferred technical solution of the present invention, the processing monitoring module includes a key parameter detection unit and an exhaust gas parameter detection unit; The key parameter detection unit is used to collect key parameters during the processing, specifically including temperature, oxygen content, humidity, and rice bran consumption rate at each sampling time, and compares them with historical data to calculate the first deviation coefficient, the specific expression of which is as follows: in, This represents the first deviation coefficient. express The first moment The specific values ​​of key parameters collected by each sensor. This indicates the maximum value of the key parameter. This represents the minimum value of the key parameter.

[0006] As a preferred technical solution of the present invention, the key parameter detection unit is based on the first deviation coefficient. The second deviation coefficient is calculated using the following expression: in, This represents the second deviation coefficient. This indicates that the summation is performed on all key parameters. express The first moment The specific values ​​of key parameters collected by each sensor. This indicates the total number of types of key parameters. The first historical moment The average value of key parameters collected by each sensor It represents the absolute value.

[0007] As a preferred embodiment of the present invention, the exhaust gas parameter detection unit reads the concentration of harmful gases before exhaust gas treatment and the concentration of harmful gases after exhaust gas treatment, and calculates the current rice bran consumption rate in the key parameters based on the concentration of harmful gases before exhaust gas treatment, as specifically expressed below: in, Indicates the rate of consumption of rice bran. Indicates the rate of consumption of rice bran. Represents the volume of the gas phase space. Indicates the molar mass of carbon. This indicates the mass fraction of carbon in rice bran.

[0008] As a preferred technical solution of the present invention, the intelligent early warning module reads... The first warning instruction is issued in time to remind the staff that the parameters are unbalanced during the processing. The staff need to inspect the equipment after the processing is completed and store the current recorded data separately. The intelligent early warning module reads At that time, wait for the key parameter detection unit to calculate the second deviation coefficient. Exceeding the preset deviation threshold If so, a second early warning instruction is issued, and the specific deviation rate is calculated, as shown in the following expression: in, This indicates the deviation rate.

[0009] As a preferred technical solution of the present invention, the intelligent early warning module compares the concentration of harmful gases after exhaust gas treatment with the corresponding harmful gas emission threshold. If the concentration exceeds the threshold, an exhaust gas early warning command is issued, dispatching personnel to carry out maintenance, and automatically connecting to the backup exhaust gas treatment pipeline.

[0010] As a preferred technical solution of the present invention, the finished product detection module is called after the preset processing time is reached to detect the processed finished product. By comparing the processed finished product with the tapping sound patterns in the historical database, the finished product deviation coefficient is calculated. When the finished product deviation coefficient exceeds the set threshold, it is determined to be unqualified and the finished product is removed. After the finished product detection module outputs that it is qualified, the cleaning equipment is called to clean the processed product.

[0011] As a preferred technical solution of the present invention, the finished product deviation coefficient The calculation expression is as follows: in, and Indicates the weighting coefficient. , Indicates the peak amplitude deviation of the audio signal. This represents the deviation increase coefficient. ,but ,like The deviation threshold was not exceeded. but ,like Exceeding the preset deviation threshold but , Indicates the root mean square deviation of the audio signal. This indicates the deviation rate.

[0012] As a preferred technical solution of the present invention, the peak amplitude deviation The specific expression is as follows: in, This represents the average of the historical maximum amplitudes. This represents the mean of the historical minimum amplitudes. This indicates the maximum amplitude of the current audio signal. This represents the minimum amplitude of the current audio signal. It represents the absolute value.

[0013] This invention also provides an intelligent control method for processing aconite root slices, based on the above-mentioned intelligent control system for processing aconite root slices, comprising the following steps: S1: Feeding material, processing aconite, and monitoring key parameters of the entire processing process through the processing monitoring module. When key parameters are abnormal, the operating status of the equipment is automatically adjusted. At the same time, the exhaust gas of the processing process is monitored, and the monitoring results and real-time monitoring values ​​of key parameters are sent to the intelligent early warning module. S2: During the smoldering process, the intelligent early warning module detects and warns of the exhaust gas of the processing process. When the exhaust gas exceeds the preset exhaust gas warning value, an early warning is issued. At the same time, the key parameters of the processing process are monitored in real time and the deviation coefficient of any key parameter is calculated in real time. When the deviation coefficient of any key parameter exceeds the preset deviation threshold, an early warning is issued. S3: After the simmering time reaches the preset value, the material is discharged and the finished product is inspected. The finished product is compared with the tapping sound in the historical database to calculate the finished product deviation coefficient. When the finished product deviation coefficient exceeds the set threshold, it is judged as unqualified and the finished product is removed. S4: After the finished product inspection module outputs "qualified", the cleaning equipment is called to clean the processed products.

[0014] Compared with the prior art, the present invention provides an intelligent control system and control method for processing aconite slices, which has the following beneficial effects: This invention utilizes a processing monitoring module to synchronously and in real-time collect and analyze key parameters such as temperature, oxygen content, humidity, and rice bran consumption rate, as well as exhaust gas components at each sampling moment. This constructs a multi-dimensional, time-series process perception capability, which can automatically adjust equipment operation when parameters are abnormal, thereby significantly improving process stability and product batch consistency and reducing reliance on experience-based operations. By employing a non-destructive perception judgment method that compares tapping sound patterns with historical databases, it can quickly identify and automatically reject unqualified products, significantly improving the detection rate. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the system framework of the present invention; Figure 2 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figures 1-2 A smart control system for processing aconite slices includes a processing monitoring module, an intelligent early warning module, and a finished product detection module; The processing monitoring module is used to monitor key parameters of the entire processing process. When key parameters are abnormal, the operating status of the equipment is automatically adjusted. At the same time, the exhaust gas of the processing process is monitored, and the monitoring results and real-time monitoring values ​​of key parameters are sent to the intelligent early warning module. Through the synchronous real-time acquisition and analysis of key parameters such as temperature, oxygen content, humidity and bran consumption rate and exhaust gas components at each sampling moment, the processing monitoring module has built a multi-dimensional and time-series process perception capability. It can automatically adjust the operation of equipment when parameters are abnormal, thereby greatly improving process stability and product batch consistency and reducing reliance on experience operation. The processing monitoring module includes a key parameter detection unit and an exhaust gas parameter detection unit; The key parameter detection unit is used to collect key parameters during the processing, specifically including temperature, oxygen content, humidity, and chaff consumption rate at each sampling time. These parameters are then compared with historical data, and a first deviation coefficient is calculated. The specific expression is as follows: in, This represents the first deviation coefficient. express The first moment The specific values ​​of key parameters collected by each sensor. This indicates the maximum value of the key parameter. This indicates the minimum value of the key parameter; the output is triggered when any sensor exhibits a significant deviation. 1.

[0018] The key parameter detection unit has a first deviation coefficient. The second deviation coefficient is calculated using the following expression: in, This represents the second deviation coefficient. This indicates that the summation is performed on all key parameters. express The first moment The specific values ​​of key parameters collected by each sensor. This indicates the total number of types of key parameters. The first historical moment The average value of key parameters collected by each sensor It represents the absolute value.

[0019] The exhaust gas parameter detection unit reads the concentrations of harmful gases before and after exhaust gas treatment, and calculates the current rice bran consumption rate as a key parameter based on the concentration of harmful gases before exhaust gas treatment. The specific expression is as follows: in, Indicates the rate of consumption of rice bran. Indicates the rate of consumption of rice bran. Represents the volume of the gas phase space. Indicates the molar mass of carbon. This indicates the mass fraction of carbon in rice bran; The intelligent early warning module is used to detect and warn of exhaust gas in the processing process. When the exhaust gas exceeds the preset exhaust gas warning value, an early warning is issued. The intelligent early warning module monitors the key parameters of the processing process in real time and calculates the deviation coefficient of any key parameter in real time. When the deviation coefficient of any key parameter exceeds the preset deviation threshold, an early warning is issued. Based on the dual mechanism of exhaust gas threshold and real-time calculated deviation coefficient, the intelligent early warning module can realize early and accurate warning and proactive intervention for exhaust gas exceeding the standard and key parameter drift, reduce environmental emissions and safety risks, and improve the environmental compliance of production. The intelligent early warning module reads The first warning instruction is issued in time to remind the staff that the parameters are unbalanced during the processing. The staff need to inspect the equipment after the processing is completed and store the current recorded data separately. The intelligent early warning module reads At that time, wait for the key parameter detection unit to calculate the second deviation coefficient. Exceeding the preset deviation threshold If so, a second early warning instruction is issued, and the specific deviation rate is calculated, as shown in the following expression: in, This indicates the deviation rate. The second warning instruction is specifically an adaptive adjustment, adjusting each key parameter of the corresponding equipment by 1% to 5% and waiting for the next sampling time. Taking oxygen content as an example, when the oxygen content exceeds / falls below a certain value... In this case, you can reduce or increase the oxygen supply by 1% to 5%.

[0020] The intelligent early warning module compares the concentration of harmful gases after exhaust gas treatment with the corresponding harmful gas emission threshold. If the concentration exceeds the threshold, it issues an exhaust gas early warning command, dispatches personnel to carry out maintenance, and automatically connects to the backup exhaust gas treatment pipeline.

[0021] The finished product inspection module is activated after the preset processing time is reached to inspect the processed finished product. By comparing the processed finished product with the tapping sound patterns in the historical database, the deviation coefficient of the finished product is calculated. When the deviation coefficient of the finished product exceeds the set threshold, it is judged as unqualified and the finished product is rejected. After the finished product inspection module outputs that it is qualified, the cleaning equipment is activated to clean the processed product.

[0022] The finished product inspection module is used to inspect the processed finished products. By comparing the processed finished products with the tapping sound patterns in the historical database, the deviation coefficient of the finished products is calculated. When the deviation coefficient of the finished products exceeds the set threshold, they are judged as unqualified and the finished products are removed. The finished product inspection module adopts a non-destructive perception judgment method that compares the tapping sound patterns with the historical database. It can quickly and online identify physical state and quality deviations and automatically remove unqualified products, significantly improving the detection rate. Finished product deviation coefficient The calculation expression is as follows: in, and Indicates the weighting coefficient. , Indicates the peak amplitude deviation of the audio signal. This represents the deviation increase coefficient. ,but ,like The deviation threshold was not exceeded. but ,like Exceeding the preset deviation threshold but , Indicates the root mean square deviation of the audio signal. This indicates the deviation rate.

[0023] Peak amplitude deviation The specific expression is as follows: in, This represents the average of the historical maximum amplitudes. This represents the mean of the historical minimum amplitudes. This indicates the maximum amplitude of the current audio signal. This represents the minimum amplitude of the current audio signal. It represents the absolute value.

[0024] This system comprehensively records production data, early warning records, and test results, facilitating traceability and process improvement. Simultaneously, it transforms experience-based judgments into quantifiable characteristics and rules, reducing reliance on individual experience and promoting standardized and large-scale application of techniques. Overall, this system can improve first-pass yield, reduce energy consumption and material waste, and mitigate safety and quality risks, thereby enhancing production efficiency and economic benefits while ensuring reduced toxicity and efficacy, thus contributing to the modernization of traditional Chinese medicine processing.

[0025] Example 1: In this embodiment, the specific data is shown in Table 1 below: Table 1 at this time Exceeding the preset deviation threshold If the value is positive, a second early warning command will be issued, and the specific deviation rate will be calculated. Meanwhile, the intelligent early warning module determines that the exhaust gas is normal. After the simmering time reaches the preset value, the material is discharged, and the finished product is tested, including the deviation coefficient of each Aconitum carmichaelii. As shown in Table 2 below: Table 2 At this point, batch 2 of Aconitum carmichaelii exceeded the set threshold of 0.105 and was rejected. Experts conducted a second evaluation, and the remaining Aconitum carmichaelii were automatically cleaned and sliced. Example 2: In this embodiment, the specific data is shown in Table 3 below: Table 3 At this point, an early warning is issued, the equipment is inspected after the current processing is completed, and the current recorded data is stored separately. Simultaneously, the intelligent early warning module determines that the exhaust gas is normal, and the material is discharged after the simmering time reaches the preset value. The processed finished product is then inspected, and the deviation coefficient of each Aconitum carmichaelii is recorded. As shown in Table 4 below: Table 4 In this embodiment, batches 1-4 of Aconitum carmichaelii were all unqualified. Batch 5 of Aconitum carmichaelii was automatically cleaned and sliced.

[0026] This invention also provides an intelligent control method for processing aconite root slices, based on the above-mentioned intelligent control system for processing aconite root slices, comprising the following steps: S1: Feeding material, processing aconite, and monitoring key parameters of the entire processing process through the processing monitoring module. When key parameters are abnormal, the operating status of the equipment is automatically adjusted. At the same time, the exhaust gas of the processing process is monitored, and the monitoring results and real-time monitoring values ​​of key parameters are sent to the intelligent early warning module. S2: During the smoldering process, the intelligent early warning module detects and warns of the exhaust gas of the processing process. When the exhaust gas exceeds the preset exhaust gas warning value, an early warning is issued. At the same time, the key parameters of the processing process are monitored in real time and the deviation coefficient of any key parameter is calculated in real time. When the deviation coefficient of any key parameter exceeds the preset deviation threshold, an early warning is issued. S3: After the simmering time reaches the preset value, the material is discharged and the finished product is inspected. The finished product is compared with the tapping sound in the historical database to calculate the finished product deviation coefficient. When the finished product deviation coefficient exceeds the set threshold, it is judged as unqualified and the finished product is removed. S4: After the finished product inspection module outputs "qualified", the cleaning equipment is called to clean the processed products.

[0027] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent control system for processing aconite root slices, characterized in that: It includes a processing monitoring module, an intelligent early warning module, and a finished product testing module; The processing monitoring module is used to monitor key parameters of the entire processing process. When a key parameter is abnormal, it automatically adjusts the operating status of the equipment. At the same time, it monitors the exhaust gas of the processing process and sends the monitoring results and the real-time monitoring values ​​of the key parameters to the intelligent early warning module. The intelligent early warning module is used to detect and warn of exhaust gas in the processing process. When the exhaust gas exceeds the preset exhaust gas warning value, an early warning is issued. The intelligent early warning module monitors the key parameters of the processing process in real time and calculates the deviation coefficient of any key parameter in real time. When the deviation coefficient of any key parameter exceeds the preset deviation threshold, an early warning is issued. The finished product inspection module is used to inspect the processed finished product. By comparing the processed finished product with the tapping sound prints in the historical database, the deviation coefficient of the finished product is calculated. When the deviation coefficient of the finished product exceeds the set threshold, it is judged as unqualified and the finished product is removed.

2. The intelligent control system for processing aconite root slices according to claim 1, characterized in that: The processing monitoring module includes a key parameter detection unit and an exhaust gas parameter detection unit; The key parameter detection unit is used to collect key parameters during the processing, specifically including temperature, oxygen content, humidity, and rice bran consumption rate at each sampling time, and compares them with historical data to calculate the first deviation coefficient, the specific expression of which is as follows: in, This represents the first deviation coefficient. express The first moment The specific values ​​of key parameters collected by each sensor. This indicates the maximum value of the key parameter. This represents the minimum value of the key parameter.

3. The intelligent control system for processing aconite root slices according to claim 2, characterized in that: The key parameter detection unit has a first deviation coefficient. The second deviation coefficient is calculated using the following expression: in, This represents the second deviation coefficient. This indicates that the summation is performed on all key parameters. express The first moment The specific values ​​of key parameters collected by each sensor. This indicates the total number of types of key parameters. The first historical moment The average value of key parameters collected by each sensor It represents the absolute value.

4. The intelligent control system for processing aconite root slices according to claim 2, characterized in that: The exhaust gas parameter detection unit reads the concentration of harmful gases before and after exhaust gas treatment, and calculates the current rice bran consumption rate among the key parameters based on the concentration of harmful gases before exhaust gas treatment. The specific expression is as follows: in, Indicates the rate of consumption of rice bran. Indicates the rate of consumption of rice bran. Represents the volume of the gas phase space. Indicates the molar mass of carbon. This indicates the mass fraction of carbon in rice bran.

5. The intelligent control system for processing aconite root slices according to claim 3, characterized in that: The intelligent early warning module reads The first warning instruction is issued in time to remind the staff that the parameters are unbalanced during the processing. The staff need to inspect the equipment after the processing is completed and store the current recorded data separately. The intelligent early warning module reads At that time, wait for the key parameter detection unit to calculate the second deviation coefficient. Exceeding the preset deviation threshold If so, a second early warning instruction is issued, and the specific deviation rate is calculated, as shown in the following expression: in, This indicates the deviation rate.

6. The intelligent control system for processing aconite root slices according to claim 4, characterized in that: The intelligent early warning module compares the concentration of harmful gases after exhaust gas treatment with the corresponding harmful gas emission threshold. If the concentration exceeds the threshold, it issues an exhaust gas early warning command, dispatches staff for maintenance, and automatically connects to the backup exhaust gas treatment pipeline.

7. The intelligent control system for processing aconite root slices according to claim 5, characterized in that: The finished product inspection module is activated after the preset processing time is reached to inspect the processed finished product. By comparing the processed finished product with the tapping sound prints in the historical database, the deviation coefficient of the finished product is calculated. When the deviation coefficient of the finished product exceeds the set threshold, it is judged as unqualified and the finished product is rejected. After the finished product inspection module outputs that it is qualified, the cleaning equipment is activated to clean the processed product.

8. The intelligent control system for processing aconite root slices according to claim 7, characterized in that: The finished product deviation coefficient The calculation expression is as follows: in, and Indicates the weighting coefficient. , Indicates the peak amplitude deviation of the audio signal. This represents the deviation increase coefficient. ,but ,like The deviation threshold was not exceeded. but ,like Exceeding the preset deviation threshold but , Indicates the root mean square deviation of the audio signal. This indicates the deviation rate.

9. The intelligent control system for processing aconite root slices according to claim 8, characterized in that: The peak amplitude deviation The specific expression is as follows: in, This represents the average of the historical maximum amplitudes. This represents the mean of the historical minimum amplitudes. This indicates the maximum amplitude of the current audio signal. This represents the minimum amplitude of the current audio signal. It represents the absolute value.

10. A method for intelligent control of processed aconite root slices, based on the intelligent control system for processed aconite root slices as described in any one of claims 1-9, characterized in that: Includes the following steps: S1: Feeding material, processing aconite, and monitoring key parameters of the entire processing process through the processing monitoring module. When key parameters are abnormal, the operating status of the equipment is automatically adjusted. At the same time, the exhaust gas of the processing process is monitored, and the monitoring results and real-time monitoring values ​​of key parameters are sent to the intelligent early warning module. S2: During the smoldering process, the intelligent early warning module detects and warns of the exhaust gas of the processing process. When the exhaust gas exceeds the preset exhaust gas warning value, an early warning is issued. At the same time, the key parameters of the processing process are monitored in real time and the deviation coefficient of any key parameter is calculated in real time. When the deviation coefficient of any key parameter exceeds the preset deviation threshold, an early warning is issued. S3: After the simmering time reaches the preset value, the material is discharged and the finished product is inspected. The finished product is compared with the tapping sound in the historical database to calculate the finished product deviation coefficient. When the finished product deviation coefficient exceeds the set threshold, it is judged as unqualified and the finished product is removed. S4: After the finished product inspection module outputs "qualified", the cleaning equipment is called to clean the processed products.

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