Traditional Chinese medicine traceability management system

Abstract

The invention relates to the technical field of information management, and discloses a traditional Chinese medicine traceability management system, which comprises a data acquisition module, a planting management module, a weeding management module, a disease and pest monitoring and prevention module, a harvesting management module and a data entry optimization module. A planting plan is automatically generated according to collected environment data, weeds are accurately removed through intelligent monitoring and data analysis, the disease and pest monitoring and control module monitors the growth condition of liquorice in real time, detects the appearing disease and pest problems and calculates the optimal pesticide use dosage, and the harvesting management module makes a harvesting plan. And the data entry optimization module is used for establishing a data entry optimization model and reasonably distributing text recognition technology entry data and operator entry data aiming at manual entry of basic data when the system is online, so that the data entry efficiency is improved, and the data entry time is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of information management, and more specifically, to a Chinese medicine traceability management system. Background Art

[0002] In the existing licorice Chinese medicinal material planting traceability system, it mainly ensures the quality of the medicinal materials at a high level and can achieve good traceability by comprehensively monitoring and recording each link in the licorice medicinal material planting process. However, the existing traceability system has shortcomings. In the data entry link, operators are required to manually input a large amount of basic data. Due to the wide variety of data and the frequent updates, the manual data input by operators is inefficient and increases the time for data entry.

[0003] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a Chinese medicine traceability management system, which collects farmland environmental data through sensors, and generates a planting plan based on the environmental data collected by the sensors to achieve precise weeding. The pest and disease monitoring and control module monitors the growth of licorice, detects pests and diseases and calculates the optimal pesticide dosage. The harvesting management module formulates a harvesting plan. The data entry optimization module establishes a data entry optimization model, and reasonably allocates data entered by text recognition technology and data entered by operators to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A Chinese medicine traceability management system includes a data acquisition module, a planting management module, a weeding management module, a pest monitoring and control module, a harvesting management module and a data entry optimization module. The data acquisition module automatically collects farmland environmental data through sensors; the planting management module automatically generates a planting plan based on the collected farmland environmental data; the weeding management module achieves accurate weed removal through intelligent monitoring and data analysis; the pest monitoring and control module detects pests and diseases in real time through sensors and cameras, establishes a pesticide dosage model, and calculates the optimal pesticide dosage; the harvesting management module formulates a harvesting plan and determines the harvesting time and method; the data entry optimization module establishes a data entry optimization model for the need to manually enter basic data when the system is online, adds text recognition technology to the system, and improves operation efficiency and data accuracy. The formula of the data entry optimization model is: , Where: Efficiency of entering all data into the model, is the total number of categories of input data, For the Class data, For the The complexity of the class data, For the The number of class data, is the amount of data entered into the text recognition model per unit time, The number of data entered into the model by the operator per unit time.

[0006] As a further solution of the present invention, the complexity of the data is composed of the number of data items and the frequency of verification required when the data is entered, wherein the formula for the complexity of the data is: , Where: The complexity of the data, is the number of data entries, The frequency of verification required during data entry. The more data items there are, the greater the complexity of the data. At the same time, the higher the frequency of verification required during data entry, the greater the complexity of the data.

[0007] As a further solution of the present invention, the data acquisition module automatically collects the environmental data of the farmland through sensors, and automatically collects and locates the geographical data of the farmland location and the licorice planting area, including the following specific contents: The data acquisition module uses sensors to collect soil moisture, farmland temperature, soil pH, light intensity, air humidity and precipitation in the licorice planting environment in real time. The soil moisture sensor can obtain the moisture status of the farmland in time, the temperature sensor monitors the temperature changes in the farmland environment, the pH sensor detects the pH value of the soil, the air humidity sensor and the light sensor monitor the environmental conditions in real time. The data acquisition module also has the ability to monitor precipitation and record changes in precipitation. Through GPS satellite positioning technology, it automatically obtains the geographical location and area information of the farmland, and creates an independent geographical model for each planting block. In order to improve the accuracy and stability of data collection, the data acquisition module supports multi-point and multi-type sensor combinations. In large areas of farmland, multiple sensor nodes are set up to collect soil and air at different locations. The sensors are interconnected through wireless networks to form a distributed data acquisition network. The data acquisition network can ensure the real-time and integrity of the data. The data acquisition module transmits the collected data to the cloud server through wireless transmission technology for subsequent data analysis and processing. The data acquisition module is equipped with self-test and calibration functions. It regularly self-tests the performance of the sensor and automatically calibrates the sensor according to the set cycle or test results to avoid inaccurate data caused by sensor errors. The communication data of all sensor nodes is encrypted to prevent data from being tampered with or leaked during transmission. At the same time, it has user authority management functions to ensure that only authorized personnel can view and modify relevant data. Through multi-level security protection measures, the privacy and integrity of the data are effectively protected.

[0008] As a further solution of the present invention, the planting management module automatically generates a planting plan, automatically adjusts it according to the environmental data collected by the sensor, and links with the agricultural machinery to automatically record the operation time, method and parameters of sowing, fertilizing and irrigation farming activities, including the following specific contents: The planting management module automatically adjusts the sowing plan based on the soil conditions and climate conditions obtained by the data acquisition module and the real-time data of the farmland environment to ensure that licorice is sown under the best conditions. At the same time, the planting management module also supports automated sowing equipment and works in conjunction with machinery to achieve precise sowing and reduce errors in manual operations. The planting management module can formulate personalized fertilization plans based on the nutritional needs of licorice at different growth stages. By real-time monitoring of the nutrient content, temperature and humidity parameters in the soil, it can automatically determine the best fertilization time and amount. The planting management module is connected to the automatic fertilization equipment to achieve automated fertilization and dynamically adjust the fertilization plan based on changes in soil nutrients. The module automatically generates an irrigation plan based on the real-time soil moisture data and meteorological conditions provided by the sensor, and controls the intelligent irrigation equipment. The irrigation plan includes irrigation time, irrigation amount and irrigation method. In dry weather, the irrigation amount is increased in time, and in the rainy season, the irrigation amount is reduced to prevent excessive water from affecting the respiration and nutrient absorption of the licorice roots. The data of the entire irrigation process is automatically recorded to ensure that the information is accurate and traceable. Through sensors and smart cameras, the growth status data of Chinese medicinal materials is obtained in real time. The growth status data includes plant height, leaf color and leaf area information. If the planting management module detects that the licorice is growing too slowly or the leaves are yellowing and withering, an early warning will be issued to remind the operator to take corresponding measures.

[0009] As a further solution of the present invention, the weed control management module realizes accurate weed control through intelligent monitoring and data analysis, including the following specific contents: The weeding management module collects licorice images through high-resolution cameras installed in the field. All plants except licorice are uniformly identified as weeds. The collected images are analyzed using a deep learning algorithm. The growth density of weeds is predicted based on different farmland environmental parameters. The weeding management module generates a weeding plan, which includes the best weeding time, weeding methods, and areas to be treated to avoid excessive weed growth that affects the quality of licorice. The formula for the weed growth density prediction model is: , Where: is the weed density per unit area, is the maximum weed density per unit area under optimal conditions, For time point, For time point Soil moisture at the moment, The soil moisture that is most suitable for weed growth is is the influence coefficient of soil moisture on weed growth, For time point The air temperature at the moment, The air temperature is most suitable for weed growth. is the influence coefficient of air temperature on weed growth.

[0010] The operation time, operation area, weeding method used, type and dosage of chemical herbicides, personnel involved in the operation and equipment information of each weeding operation are recorded and stored in the weeding management module.

[0011] As a further solution of the present invention, the pest monitoring and control module monitors the growth of licorice in real time through sensors and cameras, detects the pest problems that occur, establishes a pesticide dosage model, and calculates the optimal pesticide dosage, including the following specific contents: The growth process of licorice is monitored in real time through sensors and cameras. After detecting pests and diseases, the pest and disease monitoring and prevention module automatically analyzes the pest and disease types, determines the types and development levels of pests and diseases based on the preset database, and intelligently gives prevention and control suggestions to ensure that operators can adopt the most appropriate prevention and control measures. The pesticide dosage model adjusts the pesticide dosage by combining the pest density, ambient temperature and humidity. The formula of the pesticide dosage model is: , Where: The dosage of pesticide used is is the pest density coefficient determined by the pest occurrence frequency recorded by the sensor, is the sensitivity of licorice to pests and diseases, is the killing rate of pesticides against pests, is the ambient temperature, The most suitable application temperature for pesticides. is the effect of temperature on dose, is the ambient humidity, The most suitable humidity for pesticide application. To control the intensity of the effect of humidity on dosage.

[0012] As a further solution of the present invention, the harvesting management module has the functions of automatic harvesting reminder, harvesting data recording, mechanical harvesting coordination, quality inspection and grading, and post-harvest processing guidance, including the following specific contents: The automatic harvesting reminder function reminds the operator through the mobile terminal based on the data from the data collection module and the planting management module, combined with the optimal harvesting time, and intelligently gives the harvesting time based on historical data and expert advice to avoid delayed or premature harvesting due to climate factors. The harvesting data recording function automatically records the detailed information of each harvest, including the harvesting time, operator, harvesting area and batch number, and marks each batch of Chinese medicinal materials through the Internet of Things device. After harvesting, the scanning device enters the information and associates the data of subsequent processing links. The mechanical harvesting coordination function enables the harvesting management module to link with the harvesting equipment, automatically records the mechanical working parameters and the weight data of the harvested medicinal materials, and also detects the mechanical status and operation quality through sensors to ensure accuracy and reduce damage and waste of medicinal materials. The quality inspection and grading function conducts preliminary quality inspection of medicinal materials through the connected detection equipment during the harvesting process, detects moisture, color and weight parameters and classifies and grades them, automatically records the detection data to generate a harvesting report, ensures quality traceability and timely handles unqualified medicinal materials, and the post-harvest processing guidance function recommends appropriate preliminary processing methods based on the medicinal material variety and current environmental conditions to ensure the best storage state.

[0013] The technical effects and advantages of a Chinese medicine traceability management system of the present invention are as follows: The present invention automatically collects farmland environmental data through sensors, automatically generates a planting plan based on the environmental data collected by the sensors, and achieves accurate weed removal through intelligent monitoring and data analysis. The pest and disease monitoring and control module monitors the growth of licorice in real time through sensors and cameras, detects pest and disease problems, and calculates the optimal pesticide dosage. The harvesting management module formulates a harvesting plan and determines the time and method of harvesting. The data entry optimization module establishes a data entry optimization model for the basic data that needs to be manually entered when the system is put into operation, reasonably allocates data entered by text recognition technology and data entered by operators, improves the efficiency of data entry, and reduces the time of data entry. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The present invention is a schematic diagram of the structure of a traditional Chinese medicine traceability management system. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Example 1

[0017] The invention discloses a Chinese medicine source tracing management system, comprising a data collection module, a planting management module, a weeding management module, a pest monitoring and control module, a harvesting management module and a data entry optimization module; the data collection module automatically collects environmental data of farmland through sensors, and automatically collects and locates the location of the farmland and geographical information of licorice planting areas; the planting management module automatically generates a planting plan, automatically adjusts it according to the environmental data collected by the sensor, and automatically records the operation time, method and parameters of sowing, fertilizing and irrigation farming activities in linkage with agricultural machinery; the weeding management module realizes accurate weed removal through intelligent monitoring and data analysis; the pest monitoring and control module monitors the growth of licorice in real time through sensors and cameras, detects the pest problems that occur, and calculates the optimal pesticide dosage; the harvesting management module has the functions of automatic harvesting prompt, harvesting data recording, mechanical harvesting coordination, quality inspection and grading, and post-harvest processing guidance; the data entry optimization module establishes a data entry optimization model for the need to manually enter basic data when the system is online, thereby reducing the time for data entry and the error rate of data entry.

[0018] Furthermore, the data collection module automatically collects the environmental data of the farmland through sensors, and automatically collects and locates the location of the farmland and the geographical information of the licorice planting area, including: The data acquisition module uses sensors to collect soil moisture, farmland temperature, soil pH, light intensity, air humidity and precipitation in the licorice planting environment in real time. The soil moisture sensor obtains the moisture status of the farmland in time to help farmers decide whether irrigation is needed to prevent excessive or insufficient moisture from causing the quality of licorice to deteriorate; the temperature sensor monitors the temperature changes in the farmland environment to help adjust the farmland management strategy; the pH sensor detects the pH value of the soil. The appropriate pH value is conducive to the healthy growth of licorice; the air humidity sensor and the light sensor monitor the environmental conditions in real time. The humidity and light intensity in the air affect the growth status of licorice. Excessive air humidity affects the growth of licorice. Low light intensity will cause the medicinal materials to dry up, and excessive light intensity will accelerate water evaporation, affecting the growth rate and quality of licorice. The data acquisition module has the ability to monitor precipitation and record changes in precipitation. During the growth of licorice, excessive precipitation causes water to remain in the soil, affecting the respiration and nutrient absorption of the licorice roots. By monitoring precipitation, it helps farmers to drain or replenish water when necessary. The data acquisition module automatically obtains the geographical location and area information of the farmland through GPS satellite positioning technology, and creates an independent geographical model for each planting block. In order to improve the accuracy and stability of data collection, the data acquisition module supports multi-point and multi-type sensor combinations. In a large area of ​​farmland, multiple sensor nodes are set up to collect soil, air and meteorological data at different locations. The sensors are connected to each other through wireless networks to form a distributed data acquisition network. The data acquisition network can ensure the real-time and integrity of the data. The data acquisition module transmits the collected data to the cloud server through wireless transmission technology for subsequent data analysis and processing. In the case of poor network signals, the sensor nodes also have a data cache function, which can temporarily store the collected data and automatically upload it after the signal is restored to ensure that the data is not lost; To ensure the accuracy of data acquisition and the stability of the system, the data acquisition module is equipped with a self-test and calibration function, which regularly self-tests the performance of the sensor and automatically calibrates the sensor according to the set cycle or test results to avoid inaccurate data caused by sensor errors. If a sensor fails, a fault warning is automatically issued, and the problem is located through the intelligent diagnosis function to remind the operator to perform maintenance; The data acquisition module encrypts the communication data of all sensor nodes to prevent data from being tampered with or leaked during transmission. It also has a user authority management function to ensure that only authorized personnel can view and modify relevant data. Through multi-level security protection measures, the privacy and integrity of the data can be effectively protected.

[0019] Furthermore, the planting management module automatically generates planting plans, automatically adjusts them according to the environmental data collected by sensors, and links with agricultural machinery to automatically record the operation time, method and parameters of sowing, fertilizing and irrigation farming activities, including: The planting management module automatically adjusts the sowing plan based on the soil conditions and climate conditions obtained by the data acquisition module and the real-time data of the farmland environment to ensure that licorice is sown under the best conditions. At the same time, the planting management module also supports automated sowing equipment and works in conjunction with machinery to achieve precise sowing and reduce errors in manual operations. The planting management module can formulate personalized fertilization plans based on the nutritional needs of licorice at different growth stages. By real-time monitoring of the nutrient content, temperature and humidity parameters in the soil, it can automatically determine the best fertilization time and amount. The planting management module is connected to the automatic fertilization equipment to achieve automated fertilization and dynamically adjust the fertilization plan based on changes in soil nutrients. The module automatically generates an irrigation plan based on the real-time soil moisture data and meteorological conditions provided by the sensor, and controls the intelligent irrigation equipment. The irrigation plan includes irrigation time, irrigation amount and irrigation method. In dry weather, the irrigation amount is increased in time, and in the rainy season, the irrigation amount is reduced to prevent excessive water from affecting the respiration and nutrient absorption of the licorice roots. The data of the entire irrigation process is automatically recorded to ensure that the information is accurate and traceable. Through sensors and smart cameras, the growth status data of Chinese medicinal materials is obtained in real time. The growth status data includes plant height, leaf color and leaf area information. If the planting management module detects that the licorice is growing too slowly or the leaves are yellowing and withering, an early warning will be issued to remind the operator to take corresponding measures.

[0020] Furthermore, the weed control management module achieves precise weed control through intelligent monitoring and data analysis, including: The weeding management module collects licorice images through high-resolution cameras installed in the field. All plants except licorice are uniformly identified as weeds. The collected images are analyzed using a deep learning algorithm. The growth density of weeds is predicted based on different farmland environmental parameters. The weeding management module automatically generates a weeding plan, which includes the best weeding time, weeding method, and area to be treated to avoid excessive weed growth that affects the quality of licorice. The formula for the weed growth density prediction model is: , Where: is the weed density per unit area, is the maximum weed density per unit area under optimal conditions, For time point, For time point Soil moisture at the moment, The soil moisture that is most suitable for weed growth is is the influence coefficient of soil moisture on weed growth, For time point The air temperature at the moment, The air temperature is most suitable for weed growth. is the influence coefficient of air temperature on weed growth.

[0021] The operation time, operation area, weeding method used, type and dosage of chemical herbicides, personnel involved in the operation and equipment information of each weeding operation are recorded and stored in the weeding management module.

[0022] Furthermore, the pest monitoring and control module uses sensors and cameras to monitor the growth of licorice in real time, detect pest problems, establish a pesticide dosage model, and calculate the optimal pesticide dosage, including: The growth process of licorice is monitored in real time through sensors and cameras. After detecting pests and diseases, the pest and disease monitoring and prevention module automatically analyzes the pest and disease types, determines the types and development levels of pests and diseases based on the preset database, and intelligently gives prevention and control suggestions to ensure that operators can adopt the most appropriate prevention and control measures. The pesticide dosage model adjusts the pesticide dosage by combining the pest density, ambient temperature and humidity. The formula of the pesticide dosage model is: , Where: The dosage of pesticide used is is the pest density coefficient determined by the pest occurrence frequency recorded by the sensor, is the sensitivity of licorice to pests and diseases, is the killing rate of pesticides against pests, is the ambient temperature, The most suitable application temperature for pesticides. is the effect of temperature on dose, is the ambient humidity, The most suitable humidity for pesticide application. To control the intensity of the effect of humidity on dosage.

[0023] Furthermore, the harvesting management module has the functions of automatic harvesting reminder, harvesting data recording, mechanical harvesting coordination, quality inspection and grading, and post-harvest processing guidance, including: The automatic harvesting reminder function reminds the operator through the mobile terminal based on the data from the data collection module and the planting management module, combined with the optimal harvesting time, and intelligently gives the harvesting time based on historical data and expert advice to avoid delayed or premature harvesting due to climate factors. The harvesting data recording function automatically records the detailed information of each harvest, including the harvesting time, operator, harvesting area and batch number, and marks each batch of Chinese medicinal materials through the Internet of Things device. After harvesting, the scanning device enters the information and associates the data of subsequent processing links. The mechanical harvesting coordination function enables the harvesting management module to link with the harvesting equipment, automatically records the mechanical working parameters and the weight data of the harvested medicinal materials, and also detects the mechanical status and operation quality through sensors to ensure accuracy and reduce damage and waste of medicinal materials. The quality inspection and grading function conducts preliminary quality inspection of medicinal materials through the connected detection equipment during the harvesting process, detects moisture, color and weight parameters and classifies and grades them, automatically records the detection data to generate a harvesting report, ensures quality traceability and timely handles unqualified medicinal materials, and the post-harvest processing guidance function recommends appropriate preliminary processing methods based on the medicinal material variety and current environmental conditions to ensure the best storage state.

[0024] Furthermore, the data entry optimization module establishes a prediction entry time model for the basic data that needs to be manually entered when the system goes online, reducing the time for data entry, including: When the system is launched, a large amount of basic data needs to be entered. Relying entirely on manual operations can easily lead to a high error rate. By establishing a data entry optimization model and adding text recognition technology to the system, the time for data entry can be reduced, and operational efficiency and data accuracy can be improved. The formula for the data entry optimization model is: , Where: Efficiency of entering all data into the model, is the total number of categories of input data, For the Class data, For the The complexity of the class data, For the The number of class data, is the amount of data entered into the text recognition model per unit time, is the number of data entered into the model by the operator per unit time. The complexity of the class data meets , judged as high complexity data, if The complexity of the class data meets , judged as low complexity data, if The number of class data satisfy , it is determined to be a large batch of data. If The number of class data satisfy , it is determined to be small batch data. When the complexity of the entered data is low but the batch is large, the error rate of the operator's entry is high. Therefore, text recognition technology is used to process low-complexity and large-scale data, and the data is imported into the system in batches to improve the efficiency of data entry; when the batch of entered data is small but the complexity is high, the text recognition error rate is high. Therefore, manual entry is used for small batch and high-complexity data. By reasonably allocating data entered by text recognition technology and data entered by operators, the error rate of data entry is reduced, the efficiency of data entry is improved, and the time of data entry model is reduced; The complexity of the data is composed of the number of data items and the frequency of verification required when entering the data. The formula for the complexity of the data is: , Where: The complexity of the data, is the number of data entries, The frequency of verification required during data entry. The more data items there are, the greater the complexity of the data. At the same time, the higher the frequency of verification required during data entry, the greater the complexity of the data.

[0025] The data entry optimization module improves the efficiency of the data entry model and reduces the time of manual entry through the use of text recognition technology for low-complexity and large-volume data. The remaining complex data and small-batch data are entered manually to avoid wasting human resources on simple data. Operators enter highly complex data that is difficult to recognize text, which improves data entry efficiency. Text recognition technology reduces human errors and the error rate of entered data when processing low-complexity and large-volume data. Complex data is manually processed to ensure data accuracy.

[0026] The present invention uses a data acquisition module to automatically collect farmland environmental data through sensors. The planting management module automatically generates a planting plan based on the environmental data collected by the sensors, supports automated sowing equipment, and realizes precise sowing. The weeding management module realizes precise weed removal through intelligent monitoring and data analysis. The pest and disease monitoring and control module monitors the growth of licorice in real time through sensors and cameras, detects the pest and disease problems that occur, determines the types and development levels of the pests and diseases according to a database, gives prevention and control suggestions, and calculates the optimal pesticide dosage. The harvesting management module formulates a harvesting plan and determines the time and method of harvesting. The data entry optimization module establishes a data entry optimization model for the basic data that needs to be manually entered when the system is online, reasonably allocates data entered by text recognition technology and data entered by operators, improves the efficiency of data entry, and reduces the time of data entry.

[0027] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0028] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A Chinese medicine traceability management system, comprising a data acquisition module, a planting management module, a weeding management module, a pest monitoring and control module, a harvesting management module and a data entry optimization module, characterized in that: The data collection module automatically collects farmland environmental data through sensors; the planting management module automatically generates planting plans based on the collected farmland environmental data; the weeding management module removes weeds through intelligent monitoring and data analysis; the pest and disease monitoring and prevention module detects pests and diseases in real time through sensors and cameras, establishes a pesticide dosage model, and calculates the pesticide dosage; the harvesting management module formulates a harvesting plan and determines the harvesting time and method; The data entry optimization module establishes a data entry optimization model for the need to manually enter basic data when the system goes online, adds text recognition technology to the system, and improves operational efficiency and data accuracy. The formula of the data entry optimization model is: ， Where: Efficiency of entering all data into the model, is the total number of categories of input data, For the Class data, For the The complexity of the class data, For the The number of class data, is the amount of data entered into the text recognition model per unit time, The number of data entered into the model by the operator per unit time.

2. A Chinese medicine traceability management system according to claim 1, characterized in that: The complexity of the data is composed of the number of data items and the frequency of verification required when entering the data. The formula for the complexity of the data is: ， Where: The complexity of the data, is the number of data entries, The frequency with which data entry needs to be verified.

3. A Chinese medicine traceability management system according to claim 1, characterized in that: The pest monitoring and control module detects pests and diseases in real time through sensors and cameras, establishes a pesticide dosage model, and calculates the optimal pesticide dosage. The formula of the pesticide dosage model is: , Where: The dosage of pesticide used is is the pest density coefficient determined by the pest occurrence frequency recorded by the sensor, is the sensitivity of licorice to pests and diseases, is the killing rate of pesticides against pests, is the ambient temperature, The most suitable application temperature for pesticides. is the effect of temperature on dose, is the ambient humidity, The most suitable humidity for pesticide application. To control the intensity of the effect of humidity on dosage.

4. A Chinese medicine traceability management system according to claim 1, characterized in that: The weed control management module achieves weed removal through intelligent monitoring and data analysis, establishes a weed growth density prediction model to predict the growth density of weeds, generates a weed control plan, and avoids excessive growth of weeds that affects the quality of licorice. The formula of the weed growth density prediction model is: ， Where: is the weed density per unit area, is the maximum weed density per unit area under optimal conditions, For time point, For time point Soil moisture at the moment, The soil moisture that is most suitable for weed growth is is the influence coefficient of soil moisture on weed growth, For time point The air temperature at the moment, The air temperature is most suitable for weed growth. is the influence coefficient of air temperature on weed growth.

5. A Chinese medicine traceability management system according to claim 1, characterized in that: The data collection module is connected with the planting management module, and the data entry optimization module is connected with the planting management module, the harvesting management module, the weeding management module and the pest and disease monitoring and prevention module respectively.

6. A Chinese medicine traceability management system according to claim 1, characterized in that: The planting management module adjusts the planting plan according to the collected farmland environmental data and cooperates with machinery to realize sowing. It connects to automatic fertilization equipment according to the nutritional needs of licorice at different growth stages to realize automatic fertilization. According to soil moisture data and meteorological conditions, it controls intelligent irrigation equipment and records irrigation data. It obtains licorice growth status data through sensors and smart cameras, and issues early warnings when abnormalities are detected.

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