Automatic trajectory planning and navigation system of traditional Chinese medicinal material excavating machinery

Abstract

The invention discloses an automatic trajectory planning and navigation system of a traditional Chinese medicinal material excavating machine, relates to the technical field of traditional Chinese medicinal material excavating, and aims to solve the problems that the efficiency is low during current angelica sinensis excavating, and angelica sinensis plants are different in size due to different growth environments and characteristics, so that the excavating efficiency is low. During digging, the root hair and stem leaves of the angelica sinensis are easily damaged. Comprising a planting area calibration module used for dividing an angelica sinensis planting area and planting point positions in combination with terrain, an image acquisition module used for acquiring growth images of stems and leaves of angelica sinensis in real time, a planting environment data acquisition module used for acquiring planting environment parameters in real time, and a root hair three-dimensional distribution analysis module used for analyzing the root hair distribution condition of angelica sinensis. According to the method, the digging strategy aiming at the maturity is formulated, so that the digging efficiency and quality of the angelica sinensis are remarkably improved, damage to root hairs, stems and leaves of the angelica sinensis due to improper manual operation is avoided, and the technical problems that the efficiency is low and the angelica sinensis is easily damaged are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of medicinal material excavation, and more specifically, to an automated trajectory planning and navigation system for a Chinese medicinal material excavation machine. Background Art

[0002] At present, with the increasing demand for Chinese medicinal materials, efficient and accurate methods of digging Chinese medicinal materials have become extremely important. Among them, the digging work for angelica is still mainly done manually. When digging angelica, tools are needed to clean the soil near the rhizomes of angelica. In order to avoid accidental damage to the roots of angelica, each step needs to be carried out slowly during the digging.

[0003] Since the traditional method of digging Chinese medicinal materials often relies on manual operation, it is inefficient and easy to cause damage to Chinese medicinal materials. In addition, since the roots, stems and leaves of Angelica sinensis have different medicinal effects and certain commercial values, and the roots, stems and leaves of Angelica sinensis have different appearances due to different growth environments and characteristics, although manual digging can avoid causing excessive damage to the stems and leaves, it is difficult to provide a visual distribution of the roots, so the digging of the roots of Angelica sinensis is more laborious. Although the current method of digging angelica with mechanical equipment is not the mainstream digging method, it is efficient. However, it is difficult to avoid damage to the roots, stems and leaves of angelica during mechanical digging. Therefore, the digging of angelica plants due to different growth environments and characteristics requires an digging method targeted at maturity. Therefore, an automated excavation machinery trajectory planning and navigation method is needed to improve excavation efficiency and quality while reducing damage to Chinese medicinal materials. In view of this, we propose an automated trajectory planning and navigation system for Chinese medicinal materials excavation machinery. Summary of the invention

[0004] The purpose of the present invention is to provide an automated trajectory planning and navigation system for a Chinese medicinal material excavation machine to solve the technical problem that the current angelica excavation is inefficient. Since angelica plants have different growth environments and characteristics, resulting in different plant sizes, the roots and stems and leaves of angelica are easily damaged during excavation.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: an automated trajectory planning and navigation system for a Chinese medicinal material excavation machine, comprising a planting area calibration module for dividing angelica planting areas and planting sites in combination with terrain, an image acquisition module for real-time acquisition of angelica stem and leaf growth images, a planting environment data acquisition module for real-time acquisition of planting environment parameters, a root three-dimensional distribution analysis module for analyzing the distribution of angelica roots, a dynamic adjustment module for real-time optimization of the distribution of angelica plant planting sites in combination with angelica planting sites, a deep learning model for analyzing the maturity state of angelica in combination with the growth state of angelica stem and leaves, a mining strategy setting module for specifying a mining strategy based on the three-dimensional distribution of angelica roots, a mining trajectory planning module for planning a mining path for multiple angelica plants according to the maturity state of angelica and terrain, and a mining trajectory navigation module for real-time adjustment of the mining trajectory for navigation based on the growth of angelica plants and terrain changes; The image acquisition module is connected to the deep learning model, and is used to analyze the growth status and maturity of the stems and leaves of the angelica plants according to the growth images of the stems and leaves of the plants, and obtain the maturity distribution of the angelica plants in the planting area; The planting environment data acquisition module is connected to the dynamic adjustment module, and is used to optimize the distribution of planting sites of angelica sinensis plants in combination with the planting environment parameters, and to coordinate the growth of angelica sinensis and subsequent excavation by adjusting the intervals between angelica sinensis plants; The root three-dimensional distribution analysis module is connected to the mining strategy setting module through a deep learning model, and is used to generate a three-dimensional model of the roots of a specified plant based on the maturity information of the angelica plant, and specify different angelica mining strategies according to the three-dimensional model; The deep learning model is connected to the mining trajectory planning module through a mining strategy setting module, and is used to formulate a mining trajectory according to the mature distribution of angelica plants and in combination with the angelica mining strategy of different mature angelica plants; The mining trajectory navigation module is connected to the mining trajectory planning module and is used to update the mining trajectory navigation in real time in combination with real-time data during the mining process.

[0006] Preferably, the image acquisition module, the planting environment data acquisition module and the root three-dimensional distribution analysis module are all connected to the planting area calibration module, and are used to monitor the stems, leaves, roots and environment of the Angelica plants in the planting area.

[0007] Preferably, the dynamic adjustment module and the root three-dimensional distribution analysis module are both connected to the deep learning model, and are used to accurately generate a three-dimensional model of the roots of the Angelica sinensis plant based on the Angelica sinensis planting site data positioning.

[0008] Preferably, the deep learning model is connected to the mining strategy setting module, and the mining strategy setting module is connected to the mining trajectory planning module, which is used to perform deep learning based on the collected data to determine the growth status of the Angelica sinensis plant, so as to plan the mining strategy and mining trajectory according to the growth status of the Angelica sinensis plant.

[0009] The present invention significantly improves the excavation efficiency and quality of angelica sinensis, while greatly reducing the damage to Chinese medicinal materials. By integrating an automated trajectory planning and navigation system, the present invention replaces the traditional manual excavation method, avoiding damage to the roots, stems and leaves of angelica sinensis caused by improper manual operation. In addition, the system can formulate excavation strategies for maturity according to angelica sinensis plants with different growth environments and characteristics, thereby ensuring the integrity and commercial value of Chinese medicinal materials and solving the technical problems of low efficiency and easy damage to angelica sinensis.

[0010] An automated trajectory navigation method for a Chinese medicinal material excavation machine comprises the following steps: S1. Division of angelica planting areas; By monitoring and analyzing light intensity, humidity conditions, soil characteristics and topography, different types of planting areas are divided according to the analysis results, including high, medium and low light areas, high, medium and low humidity areas, high-quality, medium and poor soil areas, and flat, gentle slope and steep slope terrain areas; S2, optimization of planting point generation; Determine the weight of different planting area characteristics on the growth of Angelica sinensis, analyze the growth conditions of multi-characteristic fusion planting areas, build a data model, simulate different planting site plans through computers, select the optimal plan, and dynamically adjust the planting sites according to the growth conditions and environmental changes, so as to control the growth of Angelica sinensis plant stems and leaves and plan subsequent mining strategies; S3, image acquisition and monitoring; Complete the arrangement of image acquisition equipment, adjust the monitoring frequency according to the growth stage and seasonal changes of angelica, use artificial intelligence image recognition technology and big data to analyze image data, provide feedback on adjustment suggestions, and establish record archives to provide experience and lessons for subsequent planting and excavation work; S4, three-dimensional root distribution analysis; Collect data on the growth characteristics and root development patterns of Angelica sinensis, conduct geological surveys in the planting areas to determine the potential root areas, design a 3D layout plan for the resistivity imager, generate a 3D model of root distribution, adjust the monitoring frequency according to the growth stage of Angelica sinensis, analyze the monitoring data, manage the data and establish a root growth prediction model; S5. Stem and leaf analysis and growth status determination; S501, image feature extraction; Select appropriate image recognition algorithms and artificial intelligence technologies to extract the characteristic information of angelica stems and leaves, classify and organize the characteristic information such as color, shape, texture, and size, and establish a characteristic information database; S502, deep learning model assisted judgment; Collect a large amount of Angelica sinensis plant image data to train the deep learning model, and optimize the model through the model training loss function: ; in, represents the loss value, is the sample size, represents the true label, It represents the model prediction value. By verifying and optimizing the trained model, the accuracy and generalization ability of the model are improved. The extracted feature information is input into the deep learning model to judge the growth status. S6, mining strategy setting; According to different root conditions and terrain conditions, excavation strategies for shallow roots, deep roots, dense roots, sparse roots and special terrains are set respectively, including selecting appropriate excavation tools and equipment, adjusting excavation strength and depth, and adopting layered excavation or segmented excavation methods to ensure complete excavation of Chinese medicinal materials and minimal damage; S7, mining trajectory planning; S701, distance measurement; Select high-precision measuring instruments, consider the growth direction and morphology of the plants, determine the location and number of measuring points, organize and analyze the measurement data, divide the spacing between mature angelica plants into different levels, and calculate the spacing between plants using the mature angelica plant spacing formula; ; in, Indicates the spacing between mature angelica plants. and Respectively represent the coordinate positions of two mature Angelica plants; S702, distribution considerations; Using geographic information system (GIS) technology and satellite positioning system, we draw a distribution map of mature angelica plants, analyze the concentration and dispersion of mature angelica plants, and determine the preliminary excavation trajectory. The concentration index formula can be used to calculate the concentration degree: ; in, Indicates the degree of concentration, Indicates the number of Angelica sinensis plants, Indicates the area of ​​the distribution region. According to the distribution situation, the digging trajectory is adjusted to ensure that all mature Angelica plants can be covered; S703, terrain combination; Analyze the impact of different terrains on the excavation trajectory, such as flat areas, sloped areas, and complex terrain areas; in flat areas, choose straight-line excavation or regular curve excavation to increase the excavation speed; in sloped areas, consider the stability of the excavation equipment and use inclined excavation or segmented excavation; in complex terrain areas, conduct detailed terrain analysis and develop a personalized excavation trajectory plan; S704, regional adjustment; Adjust the excavation trajectory according to the characteristics of different planting areas. In high-light planting areas, the spacing between planting points is large, and the excavator's activity radius is reduced to avoid damaging healthy plants. In low-light planting areas, speed up the excavation speed appropriately, but ensure the excavation quality. Adjust the excavation depth and strength according to the soil quality and root distribution. S705, equipment adaptation; Analyze the performance and characteristics of the excavator, such as digging capacity, moving speed, and turning radius. According to the equipment parameters, adjust the curve radius and straight line length of the excavation track, and reserve a certain safety margin. The safety margin formula is as follows: ; in, represents the safety margin, is the safety factor, is the device parameter; S8, real-time update of excavation trajectory navigation; S801, information collection; Choose to place sensors and image acquisition equipment on the excavator to ensure that images of angelica plants and terrain information can be collected in real time, determine the frequency and range of information collection to meet the needs of navigation updates, and process and analyze the collected information in real time to provide accurate data support for navigation updates; S802, track update; Establish trajectory update algorithm and model, adjust mining trajectory and navigation information in real time according to plant growth and terrain changes, and update trajectory through the following formula: ; in, represents the updated trajectory, represents the last trajectory, It indicates the amount adjusted for growth and terrain changes; By analyzing the updated trajectory and navigation information, the accuracy and efficiency of mining trajectory navigation are ensured, and the mining trajectory is optimized by using artificial intelligence algorithms and dynamic programming technology to adapt to the ever-changing actual situation; S803, adjustment processing; When the growth status of plants is abnormal, timely adjust the direction of travel and excavation depth of the excavator. For areas with large terrain changes, quickly adjust the excavation strategy to ensure the safety of the excavation equipment and the smooth progress of the excavation work. Establish an emergency response mechanism to quickly respond and handle emergencies to ensure the continuity and stability of the excavation work.

[0011] Preferably, the S1 Angelica sinensis planting area division further comprises the following steps: S101, light intensity monitoring; Choose appropriate light measurement equipment to ensure accuracy and stability, conduct multi-point measurements of the planting area at different time points to obtain comprehensive light data, analyze and determine the boundaries of high, medium and low light planting areas, and use the average light intensity formula: ; in, represents the average light intensity, Indicates the light intensity value of each measurement point. For the number of measurement points, high, medium, and low light planting areas were divided according to the average light intensity; S102, humidity condition monitoring; Humidity sensors are installed based on the distribution density and location to accurately reflect humidity changes. Regular inspection and calibration are performed to ensure data accuracy. The average humidity can be calculated using the formula: ; in, represents the average humidity, Indicates the humidity value measured by each sensor, is the number of sensors; S103, soil characteristics analysis; Soil sampling ensures representative coverage of different areas and soil layers. Soil analysis instruments are used to comprehensively analyze soil sample fertility, pH, and air permeability. The comprehensive soil quality index can be calculated using the comprehensive soil quality index formula: ; Among them, it means Comprehensive soil quality index, Indicates fertility value, Indicates pH value, Indicates the air permeability value. , , is the weight coefficient of different indicators, and the planting areas are divided into high-quality, medium-quality and poor-quality soil according to the results; S104, topographic analysis; The terrain data is obtained by using drone aerial photography and field measurement technology, and the flat, gentle slope and steep slope planting area are determined by processing and analysis. The slope can be calculated by the slope calculation formula: ; in, Indicates the slope, Indicates the height difference, Indicates horizontal distance and divides terrain areas into different slope ranges.

[0012] Preferably, the S2 planting point generation optimization further includes the following steps: S201, multi-characteristic melting; Determine the weights of characteristics of different planting areas, such as the influence of light intensity, humidity conditions, soil characteristics and topography on the growth of angelica sinensis, and calculate the comprehensive influence degree through the comprehensive influence formula: ; in, Indicates the comprehensive impact degree, Indicates the light intensity impact value, Indicates the influence value of humidity conditions, Indicates the influence value of soil properties, Represents the topographic influence value, , , , The weight coefficients of different characteristics are used to analyze the growth conditions of the multi-characteristic integrated planting area and formulate a planting point generation strategy taking into account the planting density and plant spacing factors; S202, data model construction; Collect and organize data related to planting sites, select appropriate geographic information system technology and big data analysis methods to build data models; S203, data model simulation verification; Different planting point plans are set to use computer simulation technology to simulate the growth of angelica sinensis. The growth simulation formula can be used: ; in, Indicates the growth assessment value, represents the planting point variable, Indicates the light intensity, Indicates humidity conditions, Indicates soil properties. Represent the topography, analyze and evaluate the advantages and disadvantages of different plans, adjust the planting point generation strategy and select the best plan; S204, dynamic adjustment of data model; Establish a planting site monitoring system to regularly monitor the growth of angelica and environmental changes. When the growth does not meet expectations or the environment changes, analyze the reasons and determine whether the planting site needs to be adjusted. Adjust the planting site in a timely manner based on the adjustment needs to ensure that angelica is always in the best growth environment.

[0013] Preferably, the S3 image acquisition and monitoring further comprises the following steps: S301, image acquisition equipment installation; Arrange image acquisition equipment around the planting points of angelica plants in each planting area. The arrangement should be based on the planting interval between angelica plants and the impact of the upward growth of the plants on image acquisition. Use a three-point surround arrangement to arrange the image acquisition equipment. S302, real-time monitoring of angelica plants; Adjust the monitoring frequency according to the growth stage and seasonal changes of Angelica sinensis, increase the frequency during the critical period, and adjust the monitoring frequency according to the growth stage coefficient S and seasonal coefficient Make adjustments and monitor frequency ; in To adjust the coefficient, a monitoring data storage system is established to ensure that the image data is preserved for a long time for subsequent analysis reference; S303, image data analysis and feedback; Artificial intelligence image recognition technology is used to automatically identify the characteristics of plants, soil, pests and diseases in the planting area, and big data analysis methods are used to analyze trends and patterns in image data, such as plant growth rate and soil moisture changes. Plant growth rate can be calculated through the formula calculate; in, Indicates the growth rate, Indicates the later height, Indicates the previous height, Indicates a time interval; The soil moisture change rate can be expressed as ; in, represents the humidity change rate, Indicates the later humidity value, Indicates the previous humidity value, generates the stem and leaf characteristic information of the angelica plant and feeds it back to the system; S304, Recording and Archiving; Establish a record file of the planting and growth process, including image data, analysis results, and adjustment measures, and organize and summarize them regularly to provide lessons learned for future planting and excavation work.

[0014] Preferably, the S4 root three-dimensional distribution analysis further comprises the following steps: S401, three-dimensional space layout design; Collect data on the growth characteristics and root development of Angelica sinensis, conduct geological surveys on the planting area, analyze factors such as soil type, soil layer structure, and groundwater level to determine the potential root growth area, design a layout plan for resistivity imaging instruments in three-dimensional space, determine the vertical layout of resistivity imaging instruments in different soil layers, and the layout radius and interval around the plants in the horizontal direction to form a ring-shaped grid layout. The layout density can be calculated using the layout density formula: ; in, Indicates the layout density, represents the number of resistivity imagers, Indicates the layout area; S402, generating a three-dimensional root model; The roots of the angelica plants were analyzed in three dimensions using a resistivity imaging array arranged in a circular grid. The analysis data was combined to generate a three-dimensional model of the root distribution to provide support for subsequent excavation operations. S403, dynamic monitoring and adjustment; The root distribution monitoring frequency was adjusted according to the growth stage of Angelica sinensis. The monitoring frequency can be expressed as: ; in, Indicates the root distribution monitoring frequency, represents the growth stage coefficient, To adjust the coefficient, analyze the monitoring data and find abnormalities, and analyze the causes in time to adjust equipment parameters or take planting management measures; S404, Data management and application; Establish a root distribution detection data management system, including storage, query, analysis and visualization functions, and conduct comprehensive analysis and evaluation in combination with other planting management data to establish a root growth prediction model. The prediction model formula can be used: ; in, represents the root growth prediction result, Represents different planting management data variables, , , is the coefficient, It is a constant term, which provides a reference for optimization of planting point generation, mining strategy setting and mining trajectory planning.

[0015] Preferably, the S6 mining strategy setting further includes the following steps: S601, shallow root strategy; Choose a small excavator as a tool suitable for shallow root excavation. Combined with the three-dimensional model of root distribution, determine the force and depth range of shallow root excavation to avoid excessive damage to the roots. The shallow root excavation depth formula can be used; ; in, Indicates the shallow root excavation depth, represents the light intensity influence coefficient, represents the humidity condition influence coefficient, represents the soil characteristics influence coefficient, , , To adjust the coefficient, the digging speed of the small excavator is controlled to ensure the stability of the digging process and prevent the influence of soil loosening on the roots; S602, deep root strategy; Choose a high-power excavator for deep root excavation. Combined with the three-dimensional model of root distribution, adjust the angle and shape of the excavation shovel to better cut into the soil and improve excavation efficiency. Use the layered excavation method to gradually go deeper into the soil to ensure that the root system is fully excavated. The formula for the number of layered excavations is as follows: ; in, represents the number of layered mining, Indicates the total excavation depth, Indicates the depth of each layer excavation; S603, dense root strategy; Combined with the three-dimensional model of root distribution, the segmented excavation method is used to gradually clean the soil around the roots to avoid damaging the surrounding roots. In particularly dense areas, manual assisted excavation can be used to ensure the integrity of the roots. S604, dense root strategy; Choose a larger shovel for the excavator, combine it with the 3D model of root distribution, increase the excavation speed, control the excavation force, avoid unnecessary damage to the roots, observe soil changes, and adjust the excavation strategy in time to ensure the smooth progress of the excavation work; S605, Special Terrain Strategy; For special terrains such as steep slopes and gullies, select appropriate excavation equipment and tools, combine the three-dimensional model of root distribution, and adjust the angle and position of the excavation equipment according to the terrain characteristics to ensure the stability and safety of the excavation process.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention significantly improves the excavation efficiency and quality of angelica sinensis, while greatly reducing the damage to angelica sinensis. By integrating an automated trajectory planning and navigation system, the present invention replaces the traditional manual excavation method, avoiding damage to the roots, stems and leaves of angelica sinensis caused by improper manual operation. In addition, the system can formulate excavation strategies for maturity according to angelica sinensis plants with different growth environments and characteristics, thereby ensuring the integrity and commercial value of angelica sinensis and solving the technical problems of low efficiency and easy damage to angelica sinensis.

[0017] 2. The present invention realizes accurate judgment of the growth status of Angelica sinensis plants through deep learning models and big data analysis technology. It can collect and analyze the growth images of Angelica sinensis stems and leaves and planting environment parameters in real time, so as to accurately predict the maturity and root distribution of Angelica sinensis. This intelligent judgment method not only improves the accuracy of mining, but also further improves the commercial value of Angelica sinensis.

[0018] 3. The present invention is also highly flexible and adaptable, and can dynamically adjust the excavation trajectory and excavation strategy according to the characteristics of different planting areas. Whether it is a flat area, a sloping area or a complex terrain area, the optimal excavation plan can be formulated, and the excavation trajectory can be updated and adjusted in real time according to the real-time data during the excavation process, thereby ensuring the continuity and stability of the excavation work, which is conducive to application in the excavation operation of angelica. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic block diagram of the system of the present invention. DETAILED DESCRIPTION

[0020] Embodiment 1: like Figure 1 As shown, the present invention relates to an automatic trajectory planning and navigation system for a Chinese medicinal material excavation machine, comprising a planting area calibration module for dividing angelica planting areas and planting points in combination with terrain, an image acquisition module for real-time acquisition of angelica stem and leaf growth images, a planting environment data acquisition module for real-time acquisition of planting environment parameters, a root three-dimensional distribution analysis module for analyzing the distribution of angelica roots, a dynamic adjustment module for real-time optimization of the distribution of angelica plant planting points in combination with angelica planting points, a deep learning model for analyzing the maturity state of angelica in combination with the growth state of angelica stem and leaves, a mining strategy setting module for specifying a mining strategy based on the three-dimensional distribution of angelica roots, a mining trajectory planning module for planning a mining path for multiple angelica plants according to the maturity state of angelica and terrain, and a mining trajectory navigation module for real-time adjustment of the mining trajectory for navigation based on the growth of angelica plants and terrain changes; In an embodiment of the present invention, the image acquisition module is connected to the deep learning model, and is used to analyze the growth status and maturity of the stems and leaves of the angelica plants according to the growth images of the stems and leaves of the plants, and obtain the maturity distribution of the angelica plants in the planting area; In an embodiment of the present invention, the planting environment data acquisition module is connected to the dynamic adjustment module, and is used to optimize the distribution of planting sites of angelica plants in combination with the planting environment parameters, by adjusting the spacing between angelica plants to coordinate the growth of angelica and subsequent excavation; In an embodiment of the present invention, the root three-dimensional distribution analysis module is connected to the mining strategy setting module through a deep learning model, and is used to generate a three-dimensional model of the roots of a specified plant based on the maturity information of the angelica plant, and specify different angelica mining strategies according to the three-dimensional model; In an embodiment of the present invention, the deep learning model is connected to the mining trajectory planning module through a mining strategy setting module, and is used to formulate a mining trajectory according to the mature distribution of angelica plants and in combination with the angelica mining strategy of different mature angelica plants; In an embodiment of the present invention, the mining trajectory navigation module is connected to the mining trajectory planning module, and is used to update the mining trajectory navigation in real time in combination with real-time data during the mining process.

[0021] In an embodiment of the present invention, the image acquisition module, the planting environment data acquisition module and the root three-dimensional distribution analysis module are all connected to the planting area calibration module for monitoring the stems, leaves, roots and environment of the Angelica plants in the planting area.

[0022] In an embodiment of the present invention, the dynamic adjustment module and the root three-dimensional distribution analysis module are both connected to the deep learning model, and are used to accurately generate a three-dimensional model of the roots of the Angelica sinensis plant based on the Angelica sinensis planting site data positioning.

[0023] In an embodiment of the present invention, the deep learning model is connected to the mining strategy setting module, and the mining strategy setting module is connected to the mining trajectory planning module, which is used to perform deep learning based on the collected data to determine the growth status of the Angelica sinensis plant, so as to plan the mining strategy and mining trajectory according to the growth status of the Angelica sinensis plant. The present invention significantly improves the mining efficiency and quality of Angelica sinensis, while greatly reducing the damage to Chinese medicinal materials. By integrating an automated trajectory planning and navigation system, the present invention replaces the traditional manual excavation method, avoiding damage to the roots and stems and leaves of Angelica sinensis caused by improper manual operation. In addition, the system can formulate mining strategies for maturity according to Angelica sinensis plants with different growth environments and characteristics, thereby ensuring the integrity and commercial value of Chinese medicinal materials, and solving the technical problems of low efficiency and easy damage to Angelica sinensis.

[0024] Embodiment 2: As another embodiment of the present invention, an automated trajectory navigation method for a Chinese medicinal material excavation machine includes the following steps: S1. Division of angelica planting areas; By monitoring and analyzing light intensity, humidity conditions, soil characteristics and topography, different types of planting areas are divided according to the analysis results, including high, medium and low light areas, high, medium and low humidity areas, high-quality, medium and poor soil areas, and flat, gentle slope and steep slope terrain areas; S101, light intensity monitoring; Choose appropriate light measurement equipment to ensure accuracy and stability, conduct multi-point measurements of the planting area at different time points to obtain comprehensive light data, analyze and determine the boundaries of high, medium and low light planting areas, and use the average light intensity formula: ; in, represents the average light intensity, Indicates the light intensity value of each measurement point. For the number of measurement points, high, medium, and low light planting areas were divided according to the average light intensity; S102, humidity condition monitoring; Humidity sensors are installed based on the distribution density and location to accurately reflect humidity changes. Regular inspection and calibration are performed to ensure data accuracy. The average humidity can be calculated using the formula: ; in, represents the average humidity, Indicates the humidity value measured by each sensor, is the number of sensors; S103, soil characteristics analysis; Soil sampling ensures representative coverage of different areas and soil layers. Soil analysis instruments are used to comprehensively analyze soil sample fertility, pH, and air permeability. The comprehensive soil quality index can be calculated using the comprehensive soil quality index formula: ; Among them, it means Comprehensive soil quality index, Indicates fertility value, Indicates pH value, Indicates the air permeability value. , , is the weight coefficient of different indicators, and the planting areas are divided into high-quality, medium-quality and poor-quality soil according to the results; S104, topographic analysis; The terrain data is obtained by using drone aerial photography and field measurement technology, and the flat, gentle slope and steep slope planting area are determined by processing and analysis. The slope can be calculated by the slope calculation formula: ; in, Indicates the slope, Indicates the height difference, Indicates horizontal distance and divides terrain areas according to different slope ranges; S2, optimization of planting point generation; Determine the weight of different planting area characteristics on the growth of Angelica sinensis, analyze the growth conditions of multi-characteristic fusion planting areas, build a data model, simulate different planting site plans through computers, select the optimal plan, and dynamically adjust the planting sites according to the growth conditions and environmental changes, so as to control the growth of Angelica sinensis plant stems and leaves and plan subsequent mining strategies; S201, multi-characteristic melting; Determine the weights of characteristics of different planting areas, such as the influence of light intensity, humidity conditions, soil characteristics and topography on the growth of angelica sinensis, and calculate the comprehensive influence degree through the comprehensive influence formula: ; in, Indicates the comprehensive impact degree, Indicates the light intensity impact value, Indicates the influence value of humidity conditions, Indicates the influence value of soil properties, Represents the topographic influence value, , , , The weight coefficients of different characteristics are used to analyze the growth conditions of the multi-characteristic integrated planting area and formulate a planting point generation strategy taking into account the planting density and plant spacing factors; S202, data model construction; Collect and organize data related to planting sites, select appropriate geographic information system technology and big data analysis methods to build data models; S203, data model simulation verification; Different planting point plans are set to use computer simulation technology to simulate the growth of angelica sinensis. The growth simulation formula can be used: ; in, Indicates the growth assessment value, represents the planting point variable, Indicates the light intensity, Indicates humidity conditions, Indicates soil properties. Represent the topography, analyze and evaluate the advantages and disadvantages of different plans, adjust the planting point generation strategy and select the best plan; S204, dynamic adjustment of data model; Establish a planting site monitoring system to regularly monitor the growth of angelica and environmental changes. When the growth does not meet expectations or the environment changes, analyze the reasons and determine whether the planting site needs to be adjusted. Adjust the planting site in a timely manner according to the adjustment needs to ensure that angelica is always in the best growth environment; S3, image acquisition and monitoring; Complete the arrangement of image acquisition equipment, adjust the monitoring frequency according to the growth stage and seasonal changes of angelica, use artificial intelligence image recognition technology and big data to analyze image data, provide feedback on adjustment suggestions, and establish record archives to provide experience and lessons for subsequent planting and excavation work; S301, image acquisition equipment installation; Arrange image acquisition equipment around the planting points of angelica plants in each planting area. The arrangement should be based on the planting interval between angelica plants and the impact of the upward growth of the plants on image acquisition. Use a three-point surround arrangement to arrange the image acquisition equipment. S302, real-time monitoring of angelica plants; Adjust the monitoring frequency according to the growth stage and seasonal changes of Angelica sinensis, increase the frequency during the critical period, and adjust the monitoring frequency according to the growth stage coefficient S and seasonal coefficient Make adjustments and monitor frequency ; in To adjust the coefficient, a monitoring data storage system is established to ensure that the image data is preserved for a long time for subsequent analysis reference; S303, image data analysis and feedback; Artificial intelligence image recognition technology is used to automatically identify the characteristics of plants, soil, pests and diseases in the planting area, and big data analysis methods are used to analyze trends and patterns in image data, such as plant growth rate and soil moisture changes. Plant growth rate can be calculated through the formula calculate; in, Indicates the growth rate, Indicates the later height, Indicates the previous height, Indicates a time interval; The soil moisture change rate can be expressed as ; in, represents the humidity change rate, Indicates the later humidity value, Indicates the previous humidity value, generates the stem and leaf characteristic information of the angelica plant and feeds it back to the system; S304, Recording and Archiving; Establish a record file of the planting and growth process, including image data, analysis results, and adjustment measures, and regularly organize and summarize them to provide lessons learned for future planting and excavation work; S4, three-dimensional root distribution analysis; Collect data on the growth characteristics and root development patterns of Angelica sinensis, conduct geological surveys in the planting areas to determine the potential root areas, design a 3D layout plan for the resistivity imager, generate a 3D model of root distribution, adjust the monitoring frequency according to the growth stage of Angelica sinensis, analyze the monitoring data, manage the data and establish a root growth prediction model; S401, three-dimensional space layout design; Collect data on the growth characteristics and root development of Angelica sinensis, conduct geological surveys on the planting area, analyze factors such as soil type, soil layer structure, and groundwater level to determine the potential root growth area, design a layout plan for resistivity imaging instruments in three-dimensional space, determine the vertical layout of resistivity imaging instruments in different soil layers, and the layout radius and interval around the plants in the horizontal direction to form a ring-shaped grid layout. The layout density can be calculated using the layout density formula: ; in, Indicates the layout density, represents the number of resistivity imagers, Indicates the layout area; S402, generating a three-dimensional root model; The roots of the angelica plants were analyzed in three dimensions using a resistivity imaging array arranged in a circular grid. The analysis data was combined to generate a three-dimensional model of the root distribution to provide support for subsequent excavation operations. S403, dynamic monitoring and adjustment; The root distribution monitoring frequency was adjusted according to the growth stage of Angelica sinensis. The monitoring frequency can be expressed as: ; in, Indicates the root distribution monitoring frequency, represents the growth stage coefficient, To adjust the coefficient, analyze the monitoring data and find abnormalities, and analyze the causes in time to adjust equipment parameters or take planting management measures; S404, Data management and application; Establish a root distribution detection data management system, including storage, query, analysis and visualization functions, and conduct comprehensive analysis and evaluation in combination with other planting management data to establish a root growth prediction model. The prediction model formula can be used: ; in, represents the root growth prediction result, Represents different planting management data variables, , , is the coefficient, is a constant term, which provides a reference for optimization of planting point generation, mining strategy setting and mining trajectory planning; S5. Stem and leaf analysis and growth status determination; S501, image feature extraction; Select appropriate image recognition algorithms and artificial intelligence technologies to extract the characteristic information of angelica stems and leaves, classify and organize the characteristic information such as color, shape, texture, and size, and establish a characteristic information database; S502, deep learning model assisted judgment; Collect a large amount of Angelica sinensis plant image data to train the deep learning model, and optimize the model through the model training loss function: ; in, represents the loss value, is the sample size, represents the true label, It represents the model prediction value. By verifying and optimizing the trained model, the accuracy and generalization ability of the model are improved. The extracted feature information is input into the deep learning model to judge the growth status. Through deep learning models and big data analysis technology, we have achieved accurate judgment of the growth status of Angelica sinensis plants. We can collect and analyze the growth images of Angelica sinensis stems and leaves and planting environment parameters in real time, so as to accurately predict the maturity and root distribution of Angelica sinensis. This intelligent judgment method not only improves the accuracy of mining, but also further increases the commercial value of Angelica sinensis.

[0025] S6, mining strategy setting; According to different root conditions and terrain conditions, excavation strategies for shallow roots, deep roots, dense roots, sparse roots and special terrains are set respectively, including selecting appropriate excavation tools and equipment, adjusting excavation strength and depth, and adopting layered excavation or segmented excavation methods to ensure complete excavation of Chinese medicinal materials and minimal damage; S601, shallow root strategy; Choose a small excavator as a tool suitable for shallow root excavation. Combined with the three-dimensional model of root distribution, determine the force and depth range of shallow root excavation to avoid excessive damage to the roots. The shallow root excavation depth formula can be used; ; in, Indicates the shallow root excavation depth, represents the light intensity influence coefficient, represents the humidity condition influence coefficient, represents the soil characteristics influence coefficient, , , To adjust the coefficient, the digging speed of the small excavator is controlled to ensure the stability of the digging process and prevent the influence of soil loosening on the roots; S602, deep root strategy; Choose a high-power excavator for deep root excavation. Combined with the three-dimensional model of root distribution, adjust the angle and shape of the excavation shovel to better cut into the soil and improve excavation efficiency. Use the layered excavation method to gradually go deeper into the soil to ensure that the root system is fully excavated. The formula for the number of layered excavations is as follows: ; in, represents the number of layered mining, Indicates the total excavation depth, Indicates the depth of each layer excavation; S603, dense root strategy; Combined with the three-dimensional model of root distribution, the segmented excavation method is used to gradually clean the soil around the roots to avoid damaging the surrounding roots. In particularly dense areas, manual assisted excavation can be used to ensure the integrity of the roots. S604, dense root strategy; Choose a larger shovel for the excavator, combine it with the 3D model of root distribution, increase the excavation speed, control the excavation force, avoid unnecessary damage to the roots, observe soil changes, and adjust the excavation strategy in time to ensure the smooth progress of the excavation work; S605, Special Terrain Strategy; For special terrains such as steep slopes and gullies, select appropriate excavation equipment and tools, combine the three-dimensional model of root distribution, and adjust the angle and position of the excavation equipment according to the terrain characteristics to ensure the stability and safety of the excavation process; S7, mining trajectory planning; S701, distance measurement; Select high-precision measuring instruments, consider the growth direction and morphology of the plants, determine the location and number of measuring points, organize and analyze the measurement data, divide the spacing between mature angelica plants into different levels, and calculate the spacing between plants using the mature angelica plant spacing formula; ; in, Indicates the spacing between mature angelica plants. and Respectively represent the coordinate positions of two mature Angelica plants; S702, distribution considerations; Using geographic information system (GIS) technology and satellite positioning system, we draw a distribution map of mature angelica plants, analyze the concentration and dispersion of mature angelica plants, and determine the preliminary excavation trajectory. The concentration index formula can be used to calculate the concentration degree: ; in, Indicates the degree of concentration, Indicates the number of Angelica sinensis plants, Indicates the area of ​​the distribution region. According to the distribution situation, the digging trajectory is adjusted to ensure that all mature Angelica plants can be covered; S703, terrain combination; Analyze the impact of different terrains on the excavation trajectory, such as flat areas, sloped areas, and complex terrain areas; in flat areas, choose straight-line excavation or regular curve excavation to increase the excavation speed; in sloped areas, consider the stability of the excavation equipment and use inclined excavation or segmented excavation; in complex terrain areas, conduct detailed terrain analysis and develop a personalized excavation trajectory plan; S704, regional adjustment; Adjust the excavation trajectory according to the characteristics of different planting areas. In high-light planting areas, the spacing between planting points is large, and the excavator's activity radius is reduced to avoid damaging healthy plants. In low-light planting areas, speed up the excavation speed appropriately, but ensure the excavation quality. Adjust the excavation depth and strength according to the soil quality and root distribution. S705, equipment adaptation; Analyze the performance and characteristics of the excavator, such as digging capacity, moving speed, and turning radius. According to the equipment parameters, adjust the curve radius and straight line length of the excavation track, and reserve a certain safety margin. The safety margin formula is as follows: ; in, represents the safety margin, is the safety factor, is the device parameter; The present invention has high flexibility and adaptability, and can dynamically adjust the excavation trajectory and excavation strategy according to the characteristics of different planting areas. Whether it is a flat area, a sloping area or a complex terrain area, the system can formulate the optimal excavation plan. In addition, the system can also update and adjust the excavation trajectory in real time according to the real-time data during the excavation process, thereby ensuring the continuity and stability of the excavation work, which is conducive to its application in the excavation operation of angelica.

[0026] S8, real-time update of excavation trajectory navigation; S801, information collection; Choose to place sensors and image acquisition equipment on the excavator to ensure that images of angelica plants and terrain information can be collected in real time, determine the frequency and range of information collection to meet the needs of navigation updates, and process and analyze the collected information in real time to provide accurate data support for navigation updates; S802, track update; Establish trajectory update algorithm and model, adjust mining trajectory and navigation information in real time according to plant growth and terrain changes, and update trajectory through the following formula: ; in, represents the updated trajectory, represents the last trajectory, It indicates the amount adjusted for growth and terrain changes; By analyzing the updated trajectory and navigation information, the accuracy and efficiency of mining trajectory navigation are ensured, and the mining trajectory is optimized by using artificial intelligence algorithms and dynamic programming technology to adapt to the ever-changing actual situation; S803, adjustment processing; When the growth status of plants is abnormal, timely adjust the direction of travel and excavation depth of the excavator. For areas with large terrain changes, quickly adjust the excavation strategy to ensure the safety of the excavation equipment and the smooth progress of the excavation work. Establish an emergency response mechanism to quickly respond and handle emergencies to ensure the continuity and stability of the excavation work.

[0027] The embodiments of the present invention disclose preferred embodiments, but are not limited thereto. A person skilled in the art can easily understand the spirit of the present invention based on the above embodiments and make different extensions and changes. However, as long as they do not deviate from the spirit of the present invention, they are all within the protection scope of the present invention.

Claims

1. An automated trajectory planning and navigation system for a Chinese medicinal material excavation machine, characterized in that: It includes a planting area calibration module for dividing angelica planting areas and planting points in combination with terrain, an image acquisition module for real-time acquisition of angelica stem and leaf growth images, a planting environment data acquisition module for real-time acquisition of planting environment parameters, a root three-dimensional distribution analysis module for analyzing the distribution of angelica roots, a dynamic adjustment module for real-time optimization of the distribution of angelica plant planting points in combination with angelica planting points, a deep learning model for analyzing the maturity state of angelica in combination with the growth state of angelica stem and leaves, a mining strategy setting module for specifying a mining strategy based on the three-dimensional distribution of angelica roots, a mining trajectory planning module for planning mining paths for multiple angelica plants according to the maturity state of angelica and terrain, and a mining trajectory navigation module for real-time adjustment of the mining trajectory for navigation based on the growth of angelica plants and terrain changes; The image acquisition module is connected to the deep learning model, and is used to analyze the growth status and maturity of the stems and leaves of the angelica plants according to the growth images of the stems and leaves of the plants, and obtain the maturity distribution of the angelica plants in the planting area; The planting environment data acquisition module is connected to the dynamic adjustment module, and is used to optimize the distribution of planting sites of angelica sinensis plants in combination with the planting environment parameters, and to coordinate the growth of angelica sinensis and subsequent excavation by adjusting the intervals between angelica sinensis plants; The root three-dimensional distribution analysis module is connected to the mining strategy setting module through a deep learning model, and is used to generate a three-dimensional model of the roots of a specified plant based on the maturity information of the angelica plant, and specify different angelica mining strategies according to the three-dimensional model; The deep learning model is connected to the mining trajectory planning module through a mining strategy setting module, and is used to formulate a mining trajectory according to the mature distribution of angelica plants and in combination with the angelica mining strategy of different mature angelica plants; The mining trajectory navigation module is connected to the mining trajectory planning module and is used to update the mining trajectory navigation in real time in combination with real-time data during the mining process.

2. The automated trajectory planning and navigation system for a Chinese medicinal material excavation machine according to claim 1, characterized in that: The image acquisition module, the planting environment data acquisition module and the root three-dimensional distribution analysis module are all connected to the planting area calibration module, and are used to monitor the stems, leaves, roots and environment of the angelica plants in the planting area.

3. The automated trajectory planning and navigation system for a Chinese medicinal material excavation machine according to claim 1, characterized in that: The dynamic adjustment module and the root three-dimensional distribution analysis module are both connected to the deep learning model, and are used to accurately generate a three-dimensional model of the roots of the Angelica sinensis plant based on the Angelica sinensis planting site data positioning.

4. The automated trajectory planning and navigation system for a Chinese medicinal material excavation machine according to claim 3, characterized in that: The deep learning model is connected to the mining strategy setting module, and the mining strategy setting module is connected to the mining trajectory planning module, which is used to perform deep learning based on the collected data to determine the growth status of the Angelica sinensis plant, so as to plan the mining strategy and mining trajectory according to the growth status of the Angelica sinensis plant.

5. An automated trajectory navigation method for a Chinese medicinal material excavation machine, which is based on the automated trajectory planning and navigation system for a Chinese medicinal material excavation machine according to claim 4, characterized in that: The following steps are involved: S1. Division of angelica planting areas; By monitoring and analyzing light intensity, humidity conditions, soil characteristics and topography, different types of planting areas are divided according to the analysis results, including high, medium and low light areas, high, medium and low humidity areas, high-quality, medium and poor soil areas, and flat, gentle slope and steep slope terrain areas; S2, optimization of planting point generation; Determine the weight of different planting area characteristics on the growth of Angelica sinensis, analyze the growth conditions of multi-characteristic fusion planting areas, build a data model, simulate different planting site plans through computers, select the optimal plan, and dynamically adjust the planting sites according to the growth conditions and environmental changes, so as to control the growth of Angelica sinensis plant stems and leaves and plan subsequent mining strategies; S3, image acquisition and monitoring; Complete the arrangement of image acquisition equipment, adjust the monitoring frequency according to the growth stage and seasonal changes of angelica, use artificial intelligence image recognition technology and big data to analyze image data, provide feedback on adjustment suggestions, and establish record archives to provide experience and lessons for subsequent planting and excavation work; S4, three-dimensional root distribution analysis; Collect data on the growth characteristics and root development patterns of Angelica sinensis, conduct geological surveys in the planting areas to determine the potential root areas, design a 3D layout plan for the resistivity imager, generate a 3D model of root distribution, adjust the monitoring frequency according to the growth stage of Angelica sinensis, analyze the monitoring data, manage the data and establish a root growth prediction model; S5. Stem and leaf analysis and growth status determination; S501, image feature extraction; Select appropriate image recognition algorithms and artificial intelligence technologies to extract the characteristic information of angelica stems and leaves, classify and organize the characteristic information such as color, shape, texture, and size, and establish a characteristic information database; S502, deep learning model assisted judgment; Collect a large amount of Angelica sinensis plant image data to train the deep learning model, and optimize the model through the model training loss function: ； in, represents the loss value, is the sample size, represents the true label, It represents the model prediction value. By verifying and optimizing the trained model, the accuracy and generalization ability of the model are improved. The extracted feature information is input into the deep learning model to judge the growth status. S6, mining strategy setting; According to different root conditions and terrain conditions, excavation strategies for shallow roots, deep roots, dense roots, sparse roots and special terrains are set respectively, including selecting appropriate excavation tools and equipment, adjusting excavation strength and depth, and adopting layered excavation or segmented excavation methods to ensure complete excavation of Chinese medicinal materials and minimal damage; S7, mining trajectory planning; S701, distance measurement; Select high-precision measuring instruments, consider the growth direction and morphology of the plants, determine the location and number of measuring points, organize and analyze the measurement data, divide the spacing between mature angelica plants into different levels, and calculate the spacing between plants using the mature angelica plant spacing formula; ； in, Indicates the spacing between mature angelica plants. and They represent the coordinate positions of two mature Angelica sinensis plants; S702, distribution considerations; Using geographic information system (GIS) technology and satellite positioning system, we draw a distribution map of mature angelica plants, analyze the concentration and dispersion of mature angelica plants, and determine the preliminary excavation trajectory. The concentration index formula can be used to calculate the concentration degree: ； in, Indicates the degree of concentration, Indicates the number of Angelica sinensis plants, Indicates the area of ​​the distribution region. According to the distribution situation, the digging trajectory is adjusted to ensure that all mature Angelica plants can be covered; S703, terrain combination; Analyze the impact of different terrains on the excavation trajectory, such as flat areas, sloped areas, and complex terrain areas; in flat areas, choose straight-line excavation or regular curve excavation to increase the excavation speed; in sloped areas, consider the stability of the excavation equipment and use inclined excavation or segmented excavation; in complex terrain areas, conduct detailed terrain analysis and develop a personalized excavation trajectory plan; S704, regional adjustment; Adjust the excavation trajectory according to the characteristics of different planting areas. In high-light planting areas, the spacing between planting points is large, and the excavator's activity radius is reduced to avoid damaging healthy plants. In low-light planting areas, speed up the excavation speed appropriately, but ensure the excavation quality. Adjust the excavation depth and strength according to the soil quality and root distribution. S705, equipment adaptation; Analyze the performance and characteristics of the excavator, such as digging capacity, moving speed, and turning radius. According to the equipment parameters, adjust the curve radius and straight line length of the excavation track, and reserve a certain safety margin. The safety margin formula is as follows: ； in, represents the safety margin, is the safety factor, is the device parameter; S8, real-time update of excavation trajectory navigation; S801, information collection; Choose to place sensors and image acquisition equipment on the excavator to ensure that images of angelica plants and terrain information can be collected in real time, determine the frequency and range of information collection to meet the needs of navigation updates, and process and analyze the collected information in real time to provide accurate data support for navigation updates; S802, track update; Establish a trajectory update algorithm and model to adjust the mining trajectory and navigation information in real time according to plant growth and terrain changes. The trajectory update formula can be: ； in, represents the updated trajectory, represents the last trajectory, It indicates the amount adjusted for growth and terrain changes; By analyzing the updated trajectory and navigation information, the accuracy and efficiency of mining trajectory navigation are ensured, and the mining trajectory is optimized by using artificial intelligence algorithms and dynamic programming technology to adapt to the ever-changing actual situation; S803, adjustment processing; When the growth status of plants is abnormal, timely adjust the direction of travel and excavation depth of the excavator. For areas with large terrain changes, quickly adjust the excavation strategy to ensure the safety of the excavation equipment and the smooth progress of the excavation work. Establish an emergency response mechanism to quickly respond and handle emergencies to ensure the continuity and stability of the excavation work.

6. The method for automated trajectory navigation of a Chinese medicinal material excavating machine according to claim 5, characterized in that: The S1 Angelica sinensis planting area division also includes the following steps: S101, light intensity monitoring; Choose appropriate light measurement equipment to ensure accuracy and stability, conduct multi-point measurements of the planting area at different time points to obtain comprehensive light data, analyze and determine the boundaries of high, medium and low light planting areas, and use the average light intensity formula: ； in, represents the average light intensity, Indicates the light intensity value of each measurement point. For the number of measurement points, high, medium, and low light planting areas were divided according to the average light intensity; S102, humidity condition monitoring; Humidity sensors are installed based on the distribution density and location to accurately reflect humidity changes. Regular inspection and calibration are performed to ensure data accuracy. The average humidity can be calculated using the formula: ； in, represents the average humidity, Indicates the humidity value measured by each sensor, is the number of sensors; S103, soil characteristics analysis; Soil sampling ensures representative coverage of different areas and soil layers. Soil analysis instruments are used to comprehensively analyze soil sample fertility, pH, and air permeability. The comprehensive soil quality index can be calculated using the comprehensive soil quality index formula: ； Among them, it means Comprehensive soil quality index, Indicates fertility value, Indicates pH value, Indicates the air permeability value. , , is the weight coefficient of different indicators, and the planting areas are divided into high-quality, medium-quality and poor-quality soil according to the results; S104, topographic analysis; The terrain data is obtained by using drone aerial photography and field measurement technology, and the flat, gentle slope and steep slope planting area are determined by processing and analysis. The slope can be calculated by the slope calculation formula: ； in, Indicates the slope, Indicates the height difference, Indicates horizontal distance and divides terrain areas into different slope ranges.

7. The method for automated trajectory navigation of a Chinese medicinal material excavating machine according to claim 5, characterized in that: The S2 planting point generation optimization also includes the following steps: S201, multi-characteristic melting; Determine the weights of characteristics of different planting areas, such as the influence of light intensity, humidity conditions, soil characteristics and topography on the growth of angelica sinensis, and calculate the comprehensive influence degree through the comprehensive influence formula: ； in, Indicates the comprehensive impact degree, Indicates the light intensity impact value, Indicates the influence value of humidity conditions, Indicates the influence value of soil properties, Represents the topographic influence value, , , , The weight coefficients of different characteristics are used to analyze the growth conditions of the multi-characteristic integrated planting area and formulate a planting point generation strategy taking into account the planting density and plant spacing factors; S202, data model construction; Collect and organize data related to planting sites, select appropriate geographic information system technology and big data analysis methods to build data models; S203, data model simulation verification; Different planting point plans are set to use computer simulation technology to simulate the growth of angelica sinensis. The growth simulation formula can be used: ； in, Indicates the growth assessment value, represents the planting point variable, Indicates the light intensity, Indicates humidity conditions, Indicates soil properties. Represent the topography, analyze and evaluate the advantages and disadvantages of different plans, adjust the planting point generation strategy and select the best plan; S204, dynamic adjustment of data model; Establish a planting site monitoring system to regularly monitor the growth of angelica and environmental changes. When the growth does not meet expectations or the environment changes, analyze the reasons and determine whether the planting site needs to be adjusted. Adjust the planting site in a timely manner based on the adjustment needs to ensure that angelica is always in the best growth environment.

8. The method for automated trajectory navigation of a Chinese medicinal material excavating machine according to claim 5, characterized in that: The S3 image acquisition and monitoring also includes the following steps: S301, image acquisition equipment installation; Arrange image acquisition equipment around the planting points of angelica plants in each planting area. The arrangement should be based on the planting interval between angelica plants and the impact of the upward growth of the plants on image acquisition. Use a three-point surround arrangement to arrange the image acquisition equipment. S302, real-time monitoring of angelica plants; Adjust the monitoring frequency according to the growth stage and seasonal changes of Angelica sinensis, increase the frequency during the critical period, and adjust the monitoring frequency according to the growth stage coefficient S and seasonal coefficient Make adjustments and monitor frequency ; in To adjust the coefficient, a monitoring data storage system is established to ensure that the image data is preserved for a long time for subsequent analysis reference; S303, image data analysis and feedback; Artificial intelligence image recognition technology is used to automatically identify the characteristics of plants, soil, pests and diseases in the planting area, and big data analysis methods are used to analyze trends and patterns in image data, such as plant growth rate and soil moisture changes. Plant growth rate can be calculated through the formula calculate; in, Indicates the growth rate, Indicates the later height, Indicates the previous height, Indicates a time interval; The soil moisture change rate is expressed as ; in, represents the humidity change rate, Indicates the later humidity value, Indicates the previous humidity value, generates the stem and leaf characteristic information of the angelica plant and feeds it back to the system; S304, Recording and Archiving; Establish a record file of the planting and growth process, including image data, analysis results, and adjustment measures, and organize and summarize them regularly to provide lessons learned for future planting and excavation work.

9. The method for automated trajectory navigation of a Chinese medicinal material excavating machine according to claim 5, characterized in that: The S4 root three-dimensional distribution analysis also includes the following steps: S401, three-dimensional space layout design; Collect data on the growth characteristics and root development of Angelica sinensis, conduct geological surveys on the planting area, analyze factors such as soil type, soil layer structure, and groundwater level to determine the potential root growth area, design a layout plan for resistivity imaging instruments in three-dimensional space, determine the vertical layout of resistivity imaging instruments in different soil layers, and the layout radius and interval around the plants in the horizontal direction to form a ring-shaped grid layout. The layout density can be calculated using the layout density formula: ； in, Indicates the layout density, represents the number of resistivity imagers, Indicates the layout area; S402, generating a three-dimensional root model; The roots of the angelica plants were analyzed in three dimensions using a resistivity imaging array arranged in a circular grid. The analysis data was combined to generate a three-dimensional model of the root distribution to provide support for subsequent excavation operations. S403, dynamic monitoring and adjustment; The root distribution monitoring frequency was adjusted according to the growth stage of Angelica sinensis. The monitoring frequency can be expressed as: ; in, Indicates the root distribution monitoring frequency, represents the growth stage coefficient, To adjust the coefficient, analyze the monitoring data and find abnormalities, and analyze the causes in time to adjust equipment parameters or take planting management measures; S404, Data management and application; Establish a root distribution detection data management system, including storage, query, analysis and visualization functions, and conduct comprehensive analysis and evaluation in combination with other planting management data to establish a root growth prediction model. The prediction model formula can be used: ； in, represents the root growth prediction result, Represents different planting management data variables, , , is the coefficient, It is a constant term, which provides a reference for optimization of planting point generation, mining strategy setting and mining trajectory planning.

10. The method for automated trajectory navigation of a Chinese medicinal material excavating machine according to claim 5, characterized in that: The S6 mining strategy setting also includes the following steps: S601, shallow root strategy; Choose a small excavator as a tool suitable for shallow root excavation. Combined with the three-dimensional model of root distribution, determine the force and depth range of shallow root excavation to avoid excessive damage to the roots. The shallow root excavation depth formula can be used; ； in, Indicates the shallow root excavation depth, Represents the light intensity influence coefficient, represents the humidity condition influence coefficient, represents the soil characteristics influence coefficient, , , To adjust the coefficient, the digging speed of the small excavator is controlled to ensure the stability of the digging process and prevent the influence of soil loosening on the roots; S602, deep root strategy; Choose a high-power excavator for deep root excavation. Combined with the three-dimensional model of root distribution, adjust the angle and shape of the excavation shovel to better cut into the soil and improve excavation efficiency. Use the layered excavation method to gradually go deeper into the soil to ensure that the root system is fully excavated. The formula for the number of layered excavations is as follows: ； in, represents the number of layered mining, Indicates the total excavation depth, Indicates the depth of each layer excavation; S603, dense root strategy; Combined with the three-dimensional model of root distribution, the segmented excavation method is used to gradually clean the soil around the roots to avoid damaging the surrounding roots. In particularly dense areas, manual assisted excavation can be used to ensure the integrity of the roots. S604, dense root strategy; Choose a larger shovel for the excavator, combine it with the 3D model of root distribution, increase the excavation speed, control the excavation force, avoid unnecessary damage to the roots, observe soil changes, and adjust the excavation strategy in time to ensure the smooth progress of the excavation work; S605, Special Terrain Strategy; For special terrains such as steep slopes and gullies, select appropriate excavation equipment and tools, combine the three-dimensional model of root distribution, and adjust the angle and position of the excavation equipment according to the terrain characteristics to ensure the stability and safety of the excavation process.

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