Method, system, equipment and medium for evaluating the site selection of traditional Chinese medicine planting based on big data

Abstract

A method, system, equipment and medium for evaluating the site selection of traditional Chinese medicine planting based on big data, relating to the field of data processing technology. The method includes: obtaining the category of traditional Chinese medicine to be planted and soil data and meteorological data of multiple candidate planting areas; establishing a planting area evaluation standard based on plant growth parameters corresponding to the category of traditional Chinese medicine; evaluating and processing the soil data and meteorological data of each candidate planting area according to the planting area evaluation standard to obtain an environmental adaptability index; receiving multispectral images of each candidate planting area, determining the vegetation coverage and soil erosion rate of each candidate planting area, and calculating the environmental risk index; combining each environmental adaptability index and environmental risk index to determine the corresponding site selection priority, and taking the candidate planting area with the highest site selection priority as the target site selection area for the traditional Chinese medicine to be planted. Implementing the technical solution provided in this application can improve the accuracy of the site selection evaluation of traditional Chinese medicine planting.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and specifically to a method, system, equipment and medium for evaluating the site selection for Chinese medicinal plant cultivation based on big data. Background Art

[0002] The cultivation of Chinese medicinal materials is an important part of China's traditional medicine industry, and its quality directly affects the efficacy of Chinese medicine. The growth of Chinese medicinal materials is highly dependent on environmental conditions, including soil type, climate conditions, water supply and other factors. Therefore, the appropriate planting site selection is crucial to ensure the quality and yield of Chinese medicinal materials.

[0003] At present, the existing evaluation methods for the site selection of Chinese herbal medicine planting mainly rely on relevant experts to evaluate soil conditions and select sites for Chinese herbal medicine planting based on the evaluation results. However, in actual applications, since Chinese herbal medicine planting is affected by many factors, it is often difficult to fully evaluate whether the conditions for Chinese herbal medicine planting are suitable by only considering a single soil condition, thereby reducing the accuracy of the evaluation of the site selection for Chinese herbal medicine planting. Summary of the invention

[0004] The present application provides a method, system, equipment and medium for evaluating the site selection for Chinese medicinal material planting based on big data, which can improve the accuracy of the evaluation of the site selection for Chinese medicinal material planting.

[0005] In the first aspect, the present application provides a method for evaluating the site selection of traditional Chinese medicine planting based on big data, including:

[0006] Obtain the categories of Chinese medicinal materials to be planted and soil data and meteorological data of multiple candidate planting areas;

[0007] Establishing planting area assessment criteria based on plant growth parameters corresponding to the Chinese medicine categories;

[0008] According to the planting area evaluation standard, the soil data and meteorological data of each candidate planting area are evaluated and processed to obtain a corresponding environmental adaptability index;

[0009] Receiving multispectral images of each candidate planting area collected by a drone, determining the vegetation coverage and soil erosion rate of each candidate planting area based on each multispectral image, and calculating the corresponding environmental risk index based on the vegetation coverage and soil erosion rate of each candidate planting area;

[0010] The corresponding site selection priority is determined in combination with the environmental adaptability index and environmental risk index of each candidate planting area, and the candidate planting area with the highest site selection priority is used as the target site selection area for the Chinese medicinal materials to be planted.

[0011] In a second aspect of the present application, a Chinese medicine planting site selection evaluation system based on big data is provided, the system comprising:

[0012] A data acquisition module, used to acquire the categories of Chinese medicinal herbs to be planted and soil data and meteorological data of multiple candidate planting areas;

[0013] An evaluation standard determination module, used to establish a planting area evaluation standard based on the plant growth parameters corresponding to the traditional Chinese medicine category;

[0014] An index determination module is used to evaluate and process the soil data and meteorological data of each candidate planting area according to the planting area evaluation standard to obtain a corresponding environmental adaptability index; receive multispectral images of each candidate planting area collected by a drone, determine the vegetation coverage and soil erosion rate of each candidate planting area based on each multispectral image, and calculate the corresponding environmental risk index based on the vegetation coverage and soil erosion rate of each candidate planting area;

[0015] The site selection module is used to determine the corresponding site selection priority in combination with the environmental adaptability index and environmental risk index of each candidate planting area, and use the candidate planting area with the highest site selection priority as the target site selection area for the Chinese medicinal materials to be planted.

[0016] In a third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can implement a method for evaluating the site selection for Chinese medicine planting based on big data when loaded and executed by the processor.

[0017] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements a method for evaluating the site selection for Chinese medicinal material planting based on big data.

[0018] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0019] By adopting the above technical solutions, the category information of Chinese medicine to be planted and the soil and meteorological data of multiple candidate planting areas are obtained to ensure the comprehensiveness of the evaluation basis. Based on the plant growth parameters corresponding to the Chinese medicine category, a scientific planting area evaluation standard is established, and the soil and meteorological data are evaluated and processed according to the standard to obtain the environmental adaptability index, so as to accurately measure the suitability of each area. Subsequently, multispectral images are collected by drones to determine the vegetation coverage and soil erosion rate, and the environmental risk index is calculated to achieve real-time monitoring of the regional ecological status and identification of potential risks. Finally, the environmental adaptability index and environmental risk index are combined to determine the site selection priority, and the area with the highest priority is selected as the target planting site. This method not only overcomes the shortcomings of traditional single-dimensional evaluation, reduces subjectivity, improves evaluation efficiency, but also effectively controls environmental risks, thereby improving the accuracy of site selection evaluation for Chinese medicine planting. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of a method for evaluating the site selection for Chinese medicinal plant cultivation based on big data provided in an embodiment of the present application;

[0021] Figure 2 It is a structural schematic diagram of a Chinese medicine planting site selection evaluation system based on big data provided in an embodiment of the present application;

[0022] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0023] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0024] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0025] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0026] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0027] The present application embodiment provides a method for evaluating the site selection of traditional Chinese medicine planting based on big data. In one embodiment, please refer to Figure 1 , Figure 1 This is a flow chart of a method for evaluating the site selection of traditional Chinese medicine planting based on big data provided by an embodiment of the present application. The method can be implemented by a computer program, which can be integrated into an application or run as an independent tool application. The method can also be implemented by a single-chip microcomputer or run on a traditional Chinese medicine planting site selection evaluation system based on big data based on the von Neumann system. Specifically, the method can include the following steps:

[0028] Step 101: Obtain the category of the Chinese medicinal materials to be planted and soil data and meteorological data of multiple candidate planting areas.

[0029] Among them, the category of Chinese medicine refers to the specific variety of Chinese medicine to be planted, which corresponds to specific plant growth parameters, including meteorological indicators and soil indicators suitable for its growth. These parameters are stored in the preset plant growth record database and used to establish the evaluation standards for the planting area later.

[0030] Candidate planting areas refer to multiple potential Chinese medicine planting locations that can be selected. These areas need to be determined through a comprehensive assessment of the environmental adaptability index and environmental risk index to determine the final target site selection area. Each candidate planting area has its specific geographical location information, which is also used to calculate the logistics transportation distance between the Chinese medicine processing area.

[0031] Soil data specifically includes soil characteristic parameters such as organic matter content, soil water content, and soil mineral content of each candidate planting area. These data are used to match the soil characteristic value range in the planting area assessment standard to determine the soil suitability level.

[0032] Meteorological data specifically include meteorological characteristic parameters such as the average annual temperature, annual precipitation, and sunshine duration of each candidate planting area. These data are used to match the meteorological characteristic value intervals in the planting area assessment standards to determine the meteorological suitability level. At the same time, the average annual rainfall is also used to calculate the soil and water loss rate.

[0033] Specifically, it is necessary to determine the specific Chinese medicinal materials to be planted, such as ginseng, astragalus or angelica, because different Chinese medicinal materials have significant differences in the requirements for the growth environment. In order to obtain the category information of Chinese medicine, the category information of the Chinese medicine to be planted can be entered into the system by database query or manual input. At the same time, the system needs to obtain the environmental data of multiple candidate planting areas, which can be several pre-divided geographical areas, such as multiple suitable planting blocks divided in a province. For each candidate planting area, the system collects soil data through soil monitoring equipment, including parameters such as organic matter content, soil water content and soil mineral content measured by soil sensors; and collects meteorological data through meteorological stations, including parameters such as average annual temperature, annual precipitation and sunshine duration. The collection of these data can be carried out in a regular and automatic manner, and the collected data can be uploaded to the data processing center in real time. The environmental data obtained in this way has the characteristics of high accuracy and strong timeliness, and can truly reflect the environmental conditions of each candidate planting area. The collection of these basic data provides reliable data support for the subsequent establishment of evaluation standards and calculation of environmental adaptability index, which helps the system to accurately evaluate whether each candidate planting area is suitable for the planting of specific Chinese medicines. Compared with the traditional evaluation method that relies solely on expert experience, this method based on measured data is more objective and comprehensive, and can provide a more scientific decision-making basis for the site selection of Chinese medicine planting.

[0034] Step 102: Establishing a planting area assessment standard based on plant growth parameters corresponding to the Chinese medicine categories.

[0035] Among them, plant growth parameters refer to the most suitable environmental conditions parameters required for a specific category of Chinese medicine during its growth process, including suitable growth meteorological indicators and suitable growth soil indicators. Among them, suitable growth meteorological indicators include the most suitable annual average temperature, annual precipitation and sunshine duration; suitable growth soil indicators include the most suitable organic matter content, soil water content and soil mineral content. These parameters are stored in the preset plant growth record database as the basic data for establishing evaluation standards.

[0036] The planting area assessment standard refers to a standard system for grading environmental conditions based on plant growth parameters, including meteorological characteristic value intervals and soil characteristic value intervals. For example, the meteorological characteristic value interval can divide the average annual temperature, annual precipitation and sunshine duration into four levels: most suitable, relatively suitable, generally suitable and unsuitable; the soil characteristic value interval can also divide the organic matter content, soil water content and soil mineral content into four corresponding levels. This standard is used to subsequently evaluate the environmental adaptability of candidate planting areas, and the degree of suitability is determined by matching the actual environmental parameters with the standard.

[0037] Specifically, in order to evaluate the suitability of each candidate planting area, the planting area evaluation standard is first established. The system will query the corresponding plant growth parameters in the preset plant growth record database according to the determined Chinese medicine category. These plant growth parameters include suitable growth meteorological indicators and suitable growth soil indicators, which are the most suitable environmental conditions for the growth of this type of Chinese medicine summarized through long-term practice. Based on these suitable growth meteorological indicators, the system divides the meteorological conditions into multiple different meteorological suitability levels, for example, it can be divided into four levels: most suitable, more suitable, generally suitable and unsuitable. Each level corresponds to a specific meteorological characteristic value interval, including the average annual temperature interval, the annual precipitation interval and the sunshine duration interval. Similarly, based on the suitable growth soil indicators, the system also divides the soil conditions into corresponding soil suitability levels, each level corresponding to a specific soil characteristic value interval, including the organic matter content interval, the soil water content interval and the soil mineral content interval. Afterwards, the system integrates these divided meteorological characteristic value intervals and soil characteristic value intervals to form a complete planting area evaluation standard. The evaluation standards established in this way not only take into account the growth characteristics and requirements of Chinese medicinal materials, but also quantify and grade the environmental conditions, making the subsequent evaluation process more objective and standardized.

[0038] Based on the above embodiment, as an optional embodiment, in step 102: establishing a planting area assessment standard based on plant growth parameters corresponding to the traditional Chinese medicine category, this step may also include the following steps:

[0039] Step 201: In a preset plant growth record database, determine the plant growth parameters corresponding to the Chinese medicine category, where the plant growth parameters include suitable growth meteorological indicators and suitable growth soil indicators.

[0040] Specifically, it is first necessary to search for the determined Chinese medicine category identifier in the pre-established plant growth record database. For example, when determining to plant ginseng, the system will use "ginseng" as the search keyword to locate the plant growth parameter record corresponding to ginseng in the database. The record contains suitable growth meteorological indicators and suitable growth soil indicators. The suitable growth meteorological indicators specifically record that the most suitable annual average temperature for ginseng growth is 10-15℃, the annual precipitation is 800-1000mm, and the sunshine duration is 1800-2000 hours; the suitable growth soil indicators record the most suitable organic matter content of 3-5%, soil water content of 60-70%, and soil mineral content of 0.2-0.3%. These most suitable parameter values ​​will serve as the benchmark values ​​for the subsequent classification of grade intervals.

[0041] Step 202: According to the meteorological index suitable for growth, the meteorological characteristic value intervals corresponding to a plurality of different meteorological suitability levels are divided, and according to the soil index suitable for growth, the soil characteristic value intervals corresponding to a plurality of different soil suitability levels are divided.

[0042] Specifically, the system divides the levels based on the determined plant growth parameters. For the division of meteorological characteristic value intervals, taking the average annual temperature as an example, the system sets 10-15℃ as the most suitable interval, and expands upward and downward based on this: 8-10℃ and 15-17℃ are set as more suitable intervals, 6-8℃ and 17-19℃ are set as generally suitable intervals, and those below 6℃ or above 19℃ are set as unsuitable intervals. The same method is also applicable to the interval division of annual precipitation and sunshine duration. For the division of soil characteristic value intervals, taking organic matter content as an example, 3-5% is set as the most suitable interval, 2-3% and 5-6% are set as more suitable intervals, 1-2% and 6-7% are set as generally suitable intervals, and those below 1% or above 7% are set as unsuitable intervals. A similar interval division method is also used for soil moisture content and mineral content.

[0043] Step 203: Integrate the meteorological characteristic value intervals corresponding to each meteorological suitability level and the soil characteristic value intervals corresponding to each soil suitability level into a planting area assessment standard.

[0044] Specifically, the system integrates the divided meteorological characteristic value intervals and soil characteristic value intervals to form a complete evaluation standard matrix. The matrix uniformly arranges the intervals of all environmental parameters. For example, the most suitable level is recorded as level A and assigned a value of 4 points, the more suitable level is recorded as level B and assigned a value of 3 points, the general suitable level is recorded as level C and assigned a value of 2 points, and the unsuitable level is recorded as level D and assigned a value of 1 point. Such standardized processing makes the evaluation results between different environmental parameters comparable, which is convenient for the subsequent calculation of the comprehensive score. The planting area evaluation standard established in this way not only maintains the independence of the evaluation indicators, but also achieves the uniformity of the evaluation results, providing a scientific quantitative basis for the subsequent environmental adaptability evaluation. The process of establishing this standard is completely based on objective data, avoiding the deviation caused by subjective judgment and improving the accuracy of the evaluation.

[0045] Step 103: Evaluate and process the soil data and meteorological data of each candidate planting area according to the planting area evaluation standard to obtain a corresponding environmental adaptability index.

[0046] The environmental adaptability index refers to a comprehensive numerical indicator obtained through a quantitative assessment of the environmental conditions of a candidate planting area. It is used to characterize the degree of match between the environmental conditions of the region and the growth requirements of specific Chinese medicinal materials.

[0047] Specifically, in order to accurately evaluate the environmental adaptability of each candidate planting area, it is necessary to match and calculate the acquired soil data and meteorological data with the established planting area evaluation standards. The system first evaluates the meteorological data of each candidate planting area, and determines the suitability level of each meteorological parameter by comparing the annual average temperature, annual precipitation and sunshine duration of the area with the meteorological characteristic value interval. For example, the annual average temperature of a candidate planting area is 14°C, which falls into the most suitable interval (10-15°C), recorded as Class A, and scored 4 points; the annual precipitation is 750mm, which falls into the more suitable interval (700-800mm), recorded as Class B, and scored 3 points; the sunshine duration is 1850 hours, which falls into the most suitable interval (1800-2000 hours), recorded as Class A, and scored 4 points. At the same time, the system also performs a similar evaluation on soil data, comparing the organic matter content, soil water content and soil mineral content with the soil characteristic value interval, and determining the suitability level and corresponding score. After completing the evaluation of all environmental parameters, the system calculates the environmental adaptability index by weighted average, that is, the scores of each parameter are multiplied by their weight coefficients and then summed. Among them, the weight of meteorological parameters can be set to 0.6 (average annual temperature 0.25, annual precipitation 0.2, sunshine duration 0.15), and the weight of soil parameters is set to 0.4 (organic matter content 0.15, soil water content 0.15, soil mineral content 0.1). In this way, the system can obtain an environmental adaptability index between 1-4, which comprehensively reflects the degree of match between the environmental conditions of the candidate planting area and the growth requirements of traditional Chinese medicine. This evaluation method based on quantitative indicators not only takes into account the influence of various environmental factors, but also reflects the importance of different factors through weight distribution, making the evaluation results more objective and reasonable. Compared with the traditional qualitative evaluation method, this quantitative evaluation method can more accurately distinguish the differences in environmental adaptability of different candidate planting areas, providing a more reliable basis for subsequent site selection decisions.

[0048] Based on the above embodiment, as an optional embodiment, in step 103: the soil data and meteorological data of each candidate planting area are evaluated and processed according to the planting area evaluation standard to obtain the corresponding environmental adaptability index. This step may also include the following steps:

[0049] Step 301: extracting the annual average temperature, annual precipitation and sunshine duration of each candidate planting area from each meteorological data; extracting the organic matter content, soil water content and soil mineral content of each candidate planting area from each soil data.

[0050] Specifically, in order to conduct a comprehensive assessment of the candidate planting areas, it is first necessary to extract various types of environmental data. The system will call the data processing module to extract the meteorological observation data of each candidate planting area for the past three years from the meteorological data records, and calculate the average annual temperature, annual precipitation and sunshine duration. For example, the average annual temperature of a candidate planting area A is 14°C, the annual precipitation is 750mm, and the sunshine duration is 1850 hours. At the same time, the data of the most recent soil test is extracted from the soil data records, including an organic matter content of 4.5%, a soil moisture content of 65%, and a soil mineral content of 0.25%. These extracted data will serve as the basic data for subsequent evaluations.

[0051] Step 302: Match the annual average temperature, annual precipitation and sunshine duration of each candidate planting area with each meteorological characteristic value interval in the planting area evaluation standard to determine the meteorological suitability level corresponding to each candidate planting area.

[0052] Specifically, the system matches the extracted meteorological data with the meteorological characteristic value intervals in the planting area assessment criteria. Taking candidate planting area A as an example, its annual average temperature of 14°C falls within the most suitable range (10-15°C) and is judged as Class A; its annual precipitation of 750mm falls within the more suitable range (700-800mm) and is judged as Class B; its sunshine duration of 1850 hours falls within the most suitable range (1800-2000 hours) and is judged as Class A. The system will temporarily store these judgment results for subsequent comprehensive calculations.

[0053] Step 303: Match the organic matter content, soil water content and soil mineral content of each candidate planting area with each soil characteristic value interval in the planting area assessment standard to determine the soil suitability grade corresponding to each candidate planting area.

[0054] Specifically, the system performs similar matching processing on soil data. For candidate planting area A, its organic matter content of 4.5% falls within the most suitable range (3-5%) and is determined to be Class A; the soil water content of 65% falls within the most suitable range (60-70%) and is determined to be Class A; the soil mineral content of 0.25% falls within the most suitable range (0.2-0.3%) and is determined to be Class A. The determination results of these soil suitability levels will also be temporarily stored.

[0055] Step 304: Based on the preset meteorological factor weights and soil factor weights, weighted calculation is performed on the meteorological suitability level and soil suitability level of each candidate planting area to obtain a corresponding environmental adaptability index.

[0056] Specifically, the system performs comprehensive calculations based on pre-set weight parameters. The total weight of meteorological factors is 0.6, of which the weight of annual average temperature is 0.25, the weight of annual precipitation is 0.2, and the weight of sunshine duration is 0.15; the total weight of soil factors is 0.4, of which the weight of organic matter content is 0.15, the weight of soil water content is 0.15, and the weight of soil mineral content is 0.1. Convert each suitability level into a corresponding score (4 points for level A, 3 points for level B, 2 points for level C, and 1 point for level D), and then perform weighted calculations. Taking candidate planting area A as an example, the calculation formula for its environmental adaptability index is: 4×0.25+3×0.2+4×0.15+4×0.15+4×0.15+4×0.1=3.8. This weighted calculation method based on multiple environmental factors not only takes into account the differences in the importance of different environmental factors, but also realizes the quantitative expression of the evaluation results, so that the environmental adaptability between different candidate planting areas can be objectively compared. In this way, the system can calculate an environmental adaptability index between 1 and 4 for each candidate planting area, providing a basis for subsequent planting area selection.

[0057] Step 104: Receive multispectral images of each candidate planting area collected by the drone, determine the vegetation coverage and soil erosion rate of each candidate planting area based on each multispectral image, and calculate the corresponding environmental risk index based on the vegetation coverage and soil erosion rate of each candidate planting area.

[0058] Among them, the environmental risk index is a comprehensive indicator calculated based on the vegetation coverage and soil erosion rate of each candidate planting area.

[0059] Specifically, in order to comprehensively evaluate the environmental risk status of candidate planting areas, the system uses a multispectral camera equipped with a drone to collect images of each candidate planting area. The drone collects multispectral image data including red band (630-690nm), near infrared band (760-900nm) and red edge band (730-740nm) under the parameter settings of a flight altitude of 500 meters, a heading overlap of 80%, and a lateral overlap of 60%. After the acquisition is completed, the system first pre-processes the acquired multispectral images, including geometric correction, radiation calibration and atmospheric correction to ensure the accuracy of the data. Subsequently, the system uses the processed multispectral data to calculate the normalized vegetation index (NDVI). The specific calculation formula is NDVI=(near infrared band reflectance-red band reflectance) / (near infrared band reflectance+red band reflectance). Based on the NDVI value, the system uses a hierarchical statistical method to determine the vegetation coverage of each candidate planting area, that is, the percentage of vegetation coverage area to the total area. For example, when the NDVI value is greater than 0.5, it is determined to be covered with vegetation, and the area ratio of these areas is the vegetation coverage. At the same time, the system combines the red edge band data and terrain data of the multispectral image, and uses the general soil loss equation to calculate the soil and water loss rate. The calculation formula is A=R×K×L×S×C×P, where A is the average annual soil loss (t / hm²·year), R is the rainfall erosion factor, K is the soil erodibility factor, L is the slope length factor, S is the slope factor, C is the vegetation coverage factor (converted from the NDVI value), and P is the soil and water conservation measures factor. The soil and water loss rate of each candidate planting area can be obtained through this formula. After obtaining the vegetation coverage and soil and water loss rate, the system uses a comprehensive evaluation method to calculate the environmental risk index. First, the vegetation coverage and soil and water loss rate are standardized to unify their value ranges to the 0-1 range. Then, weighted calculations are performed based on the preset weight coefficients (vegetation coverage weight 0.4, soil and water loss rate weight 0.6). The calculation formula of environmental risk index is: Risk = 0.4 × (1-V) + 0.6 × E, where V is the standardized vegetation coverage and E is the standardized soil erosion rate. The obtained environmental risk index is between 0 and 1, and the larger the value, the higher the environmental risk.

[0060] This assessment method based on multispectral remote sensing and quantitative calculation is more efficient and accurate than traditional field surveys. The high-resolution multispectral data collected by drones can fully understand the vegetation and water and soil conditions in the candidate planting areas.

[0061] Based on the above embodiment, as an optional embodiment, in step 104: determining the vegetation coverage and soil erosion rate of each candidate planting area based on each multispectral image, this step may also include the following steps:

[0062] Step 401: extracting corresponding red light band reflectance values ​​and near infrared band reflectance values ​​from each multispectral image; normalizing each red light band reflectance value and each near infrared band reflectance value to obtain a vegetation index corresponding to each candidate planting area.

[0063] Specifically, in order to accurately assess the vegetation conditions and soil erosion risks in candidate planting areas, the system first processes the multispectral image data collected by drones. The system extracts the reflectance value data of the red light band and the near-infrared band from the multispectral images, respectively. These band data can effectively reflect the growth conditions of surface vegetation. For each candidate planting area, the system normalizes the extracted reflectance value data, that is, divides the red light band reflectance value and the near-infrared band reflectance value by their corresponding maximum values, so that the data range is unified to between zero and one. Then, the system uses the normalized data to calculate the vegetation index, and characterizes the growth conditions of vegetation by the difference in reflectance values ​​between the near-infrared band and the red light band. This method can effectively eliminate the influence of the atmosphere and soil background and improve the accuracy of vegetation monitoring.

[0064] Step 402: Determine the vegetation coverage of the corresponding candidate planting area based on the ratio between each vegetation index and the sum of each vegetation index.

[0065] Specifically, the vegetation coverage is evaluated based on the calculated vegetation index. First, the vegetation index of each candidate planting area is accumulated to obtain the sum of the vegetation index. Then, the vegetation index of each candidate planting area is divided by the sum of the vegetation index to obtain the vegetation coverage of the area. For example, the vegetation index of a candidate planting area is 0.6, and the sum of the vegetation indexes of all candidate planting areas is 2, then the vegetation coverage of the area is 30%. This method based on proportional calculation can intuitively reflect the relative level of vegetation coverage of each candidate planting area, which is convenient for comparative analysis between regions.

[0066] Step 403: extract corresponding terrain elevation values ​​and surface slope values ​​from the multispectral images of each candidate planting area; receive the average annual rainfall of each candidate planting area, and determine the corresponding soil and water loss rate based on the terrain elevation values, surface slope values ​​and average annual rainfall of each candidate planting area.

[0067] Specifically, the system evaluates the soil erosion situation. First, the terrain elevation data and surface slope data of each candidate planting area are extracted from the multispectral image, which reflect the undulating characteristics of the terrain. At the same time, the system receives the average annual rainfall data of each candidate planting area provided by the meteorological department. After obtaining these basic data, the system uses a comprehensive evaluation method to calculate the soil erosion rate. Specifically, when the terrain elevation value is higher and the surface slope value is larger, the risk of soil erosion is higher; at the same time, the greater the average annual rainfall, the greater the risk of soil erosion. The system weights the influence of these three factors, where the weight of the terrain elevation value is 0.3, the weight of the surface slope value is 0.4, and the weight of the average annual rainfall is 0.3. After standardizing each indicator and performing weighted summation, a quantitative indicator reflecting the degree of soil erosion can be obtained. For example, if the standardized terrain elevation value of a candidate planting area is 0.5, the surface slope value is 0.7, and the average annual rainfall index is 0.4, then its soil erosion rate is 0.55. This comprehensive assessment method based on multi-source data takes into account both the positive effect of vegetation cover on soil conservation and the impact of natural factors such as terrain characteristics and rainfall on soil erosion, making the assessment results more comprehensive.

[0068] Based on the above embodiment, as an optional embodiment, in step 104: calculating the corresponding environmental risk index based on the vegetation coverage and soil erosion rate of each candidate planting area, this step may also include the following steps:

[0069] Step 404: Obtain the planting area of ​​each candidate planting area; and determine the baseline environmental risk index corresponding to each planting area based on a preset index mapping table.

[0070] Specifically, the system first obtains the actual planting area data of each candidate planting area through drone aerial photography technology, and the area data obtained is in mu. Then, the system matches the obtained planting area with the preset index mapping table to determine the corresponding benchmark environmental risk index. The specific mapping relationship of the index mapping table is: when the planting area is 0-100 mu, the benchmark environmental risk index is 0.2; when the planting area is 101-500 mu, the benchmark environmental risk index is 0.4; when the planting area is 501-1000 mu, the benchmark environmental risk index is 0.6; when the planting area is 1001-2000 mu, the benchmark environmental risk index is 0.8; when the planting area is greater than 2000 mu, the benchmark environmental risk index is 1.0. Through this hierarchical mapping method, the system establishes a quantitative relationship between the planting scale and the benchmark environmental risk, providing basic data for subsequent risk assessment.

[0071] Step 405: Multiply the baseline environmental risk index, vegetation coverage, and soil erosion rate of each candidate planting area to obtain the environmental risk index corresponding to each candidate planting area.

[0072] Specifically, the obtained baseline environmental risk index is multiplied with the vegetation coverage and soil erosion rate data of each candidate planting area to obtain the final environmental risk index. Among them, the vegetation coverage is calculated by processing the multispectral image data obtained by the drone, and the value range is 0-1. The larger the value, the higher the vegetation coverage; the soil erosion rate is calculated based on factors such as terrain slope and rainfall, and the value range is also 0-1. The larger the value, the more serious the soil erosion. For example, when the planting area of ​​a candidate planting area is 800 mu, the corresponding baseline environmental risk index is 0.6. If the vegetation coverage of the area is 0.4 and the soil erosion rate is 0.5, the final environmental risk index calculation result of the area is 0.6×0.4×0.5=0.12. In this way, the system obtains the environmental risk assessment results that comprehensively consider the planting scale, vegetation status and soil erosion impact.

[0073] Step 105: Determine the corresponding site selection priority in combination with the environmental adaptability index and environmental risk index of each candidate planting area, and use the candidate planting area with the highest site selection priority as the target site selection area for planting Chinese medicinal materials.

[0074] Among them, the site selection priority is a ranking result calculated by the system based on the environmental adaptability index and environmental risk index. The system first standardizes these two indicators, and then uses weights to calculate the environmental adaptability index and environmental risk index respectively to obtain a comprehensive score for each candidate planting area.

[0075] The target site selection area refers to the area with the highest comprehensive score and the highest site selection priority among all candidate planting areas. This area has good environmental adaptability and can meet the growth needs of the Chinese medicine to be planted. At the same time, the environmental risk is relatively low and it is suitable for standardized planting of Chinese medicine. The system determines this area as the final Chinese medicine planting area as the implementation site for subsequent planting activities.

[0076] Specifically, the environmental adaptability index and environmental risk index are first standardized. Since the original value range of the environmental adaptability index is 1-4, the system uses a linear normalization method to convert it to the interval of 0-1. The conversion process maintains the characteristic that the larger the value, the better the adaptability. For the environmental risk index, since its original value is already between 0-1, and the larger the value, the higher the risk, the system directly uses 1 minus the original value as the standardized result, so that it has the same positive indicator characteristics as the environmental adaptability index. After completing the standardization of the indicators, the system performs a weighted calculation on the two indicators to determine the site selection priority. Considering that environmental adaptability is the key factor in determining the success of Chinese medicine planting, and environmental risk is an auxiliary factor affecting the stability of planting, the system sets the weight of the environmental adaptability index to 0.6 and the weight of the environmental risk index to 0.4. By multiplying the two standardized indicators with the corresponding weights and summing them, the comprehensive score of each candidate planting area is obtained. For example, if the standardized environmental adaptability index of a candidate planting area is 0.85 and the environmental risk index is 0.7, then its comprehensive score is 0.85×0.6+0.7×0.4=0.79. The system determines the site selection priority of each candidate planting area according to the comprehensive score. The candidate planting area with a higher score has a higher priority. The candidate planting area with the highest comprehensive score will be determined as the target site selection area for the Chinese medicine to be planted. This site selection method based on multi-indicator weighted scoring not only ensures the matching degree between the planting environment and the growth requirements of Chinese medicine, but also fully considers the impact of regional environmental risks, and can provide the best site selection plan for Chinese medicine planting. Through this site selection method, the system can select the most suitable target site selection area from many candidate planting areas. This site selection method not only improves the success rate of Chinese medicine planting, but also provides a basis for subsequent planting management work, which helps to achieve standardized planting of Chinese medicinal materials.

[0077] Based on the above embodiment, as an optional embodiment, in step 105: combining the environmental adaptability index and environmental risk index of each candidate planting area to determine the corresponding site selection priority, this step may also include the following steps:

[0078] Step 501: Based on the location information of the Chinese medicinal material processing area and the location information of each candidate planting area, calculate the logistics transportation distance from each candidate planting area to the Chinese medicinal material processing area.

[0079] Specifically, the system obtains the longitude and latitude coordinates of the Chinese medicinal materials processing area and the longitude and latitude coordinates of each candidate planting area through the geographic information positioning module. Based on these coordinate information, the system uses the spherical distance calculation formula to calculate the straight-line distance between the two points, and combines the actual road distribution to calculate the actual logistics transportation distance through the path planning algorithm. In the specific calculation process, the system first calculates the spherical distance between the two points, and then multiplies the distance by the road curvature coefficient of 1.2 to obtain a logistics transportation distance that is closer to the actual situation. For example, the longitude and latitude coordinates of a candidate planting area are (30.5°N, 114.3°E), and the longitude and latitude coordinates of the Chinese medicinal materials processing area are (30.8°N, 114.5°E). The calculated spherical distance is 35 kilometers, and the final logistics transportation distance is 42 kilometers.

[0080] Step 502: Based on the preset transportation cost coefficient and each logistics transportation distance, determine the logistics cost index corresponding to each candidate planting area.

[0081] Specifically, the system calculates the logistics cost index of each candidate planting area based on the preset transportation cost coefficient. The transportation cost coefficient is set to 0.01 per kilometer, that is, when the logistics transportation distance is 100 kilometers, the logistics cost index is 1.0. The system multiplies the calculated logistics transportation distance by the transportation cost coefficient to obtain the logistics cost index corresponding to each candidate planting area. For example, for a candidate planting area with a logistics transportation distance of 42 kilometers, the logistics cost index calculation result is 42×0.01=0.42. In this way, the system converts the logistics transportation distance into a standardized cost indicator, which is convenient for subsequent comprehensive evaluation with other indicators.

[0082] Step 503: taking the ratio of the environmental adaptability index to the environmental risk index of each candidate planting area as the comprehensive environmental index; performing weighted calculation on the comprehensive environmental index and the logistics cost index of each candidate planting area to obtain the site selection priority of each candidate planting area.

[0083] Specifically, the system first calculates the comprehensive environmental index of each candidate planting area. The specific method is to divide the environmental adaptability index by the environmental risk index. For example, if the environmental adaptability index of a candidate planting area is 0.85 and the environmental risk index is 0.12, then its comprehensive environmental index is 0.85 / 0.12=7.08. Then, the system performs weighted calculations on the comprehensive environmental index and the logistics cost index to obtain the final site selection priority. Considering that environmental factors are more important to Chinese medicine planting than logistics costs, the system sets the weight of the comprehensive environmental index to 0.7 and the weight of the logistics cost index to 0.3. For the above candidate planting areas, the comprehensive environmental index is 7.08 and the logistics cost index is 0.42. The final site selection priority calculation result is 7.08×0.7-0.42×0.3=4.834. The minus sign indicates the negative impact of the logistics cost index on the site selection priority, that is, the higher the logistics cost, the lower the site selection priority. Through this calculation method, the system takes into account the importance of environmental factors and the actual impact of logistics costs, thereby obtaining a more reasonable site priority evaluation result.

[0084] Based on the above embodiment, as an optional embodiment, in step 105: taking the candidate planting area with the highest site selection priority as the target site selection area for planting Chinese medicinal materials, after this step, the following steps may also be included:

[0085] Step 106: Obtain the Chinese medicinal herb planting density in the target site selection area; when the Chinese medicinal herb planting density exceeds the planting density threshold, generate warning information, and send the warning information to the Chinese medicinal herb planting supervision department.

[0086] Specifically, the system obtains the TCM planting density data of the target site selection area by combining land sampling and drone aerial photography. In specific implementation, the system first divides several 10m×10m standard sampling areas in the target site selection area, and uses the systematic sampling method to determine the sampling point location. In each sampling area, the staff counts the number of TCM plants on the spot and calculates the number of plants per unit area. At the same time, the system uses a drone equipped with a multispectral camera to take aerial photos of the entire target site selection area to obtain remote sensing images of vegetation coverage and distribution. The system uses the field sampling data as training samples, and uses a deep learning algorithm to analyze and process the remote sensing images to obtain a TCM planting density distribution map of the entire area, and then calculates the average planting density value. The system compares the calculated TCM planting density with the preset planting density threshold. The planting density threshold is set based on the growth characteristics of the TCM variety and the land carrying capacity. For example, the planting density threshold of a certain TCM is set to 4 plants per square meter. When the system detects that the actual planting density in the target site selection area exceeds this threshold, it will automatically generate early warning information including the location of super-dense planting, the degree of super-dense planting, and the possible environmental impact. The system pushes the early warning information to the supervision platform of the Chinese medicine planting supervision department in real time through the mobile communication network, and sends SMS reminders to the supervisor's mobile terminal. For example, when it is detected that the planting density in a certain area reaches 5 plants per square meter, the system will generate an early warning message "The planting density of area A (specific coordinates) exceeds the standard by 25%, and it is recommended to adjust the density in time." This early warning mechanism based on real-time monitoring can help the supervision department to timely discover and deal with the problem of over-dense planting, avoid the adverse effects such as intensified nutrient competition, accelerated spread of pests and diseases, and soil degradation caused by excessive planting density, thereby ensuring the planting quality of Chinese medicinal materials and the sustainability of the ecological environment.

[0087] Reference Figure 2 , a Chinese medicine planting site selection evaluation system based on big data is provided in the embodiment of the present application, the system comprises: a data acquisition module, an evaluation standard determination module, an index determination module, and a site selection module, wherein:

[0088] A data acquisition module, used to acquire the categories of Chinese medicinal herbs to be planted and soil data and meteorological data of multiple candidate planting areas;

[0089] An evaluation standard determination module is used to establish evaluation standards for planting areas based on plant growth parameters corresponding to traditional Chinese medicine categories;

[0090] The index determination module is used to evaluate and process the soil data and meteorological data of each candidate planting area according to the planting area evaluation standard to obtain the corresponding environmental adaptability index; receive the multispectral images of each candidate planting area collected by the drone, determine the vegetation coverage and soil erosion rate of each candidate planting area based on each multispectral image, and calculate the corresponding environmental risk index based on the vegetation coverage and soil erosion rate of each candidate planting area;

[0091] The site selection module is used to determine the corresponding site selection priority based on the environmental adaptability index and environmental risk index of each candidate planting area, and select the candidate planting area with the highest site selection priority as the target site selection area for the Chinese medicinal materials to be planted.

[0092] On the basis of the above embodiment, the evaluation standard determination module is also used to determine the plant growth parameters corresponding to the Chinese medicine category in a preset plant growth record database, the plant growth parameters including suitable growth meteorological indicators and suitable growth soil indicators; according to the suitable growth meteorological indicators, the meteorological characteristic value intervals corresponding to multiple different meteorological suitability levels are divided, and according to the suitable growth soil indicators, the soil characteristic value intervals corresponding to multiple different soil suitability levels are divided; the meteorological characteristic value intervals corresponding to each meteorological suitability level and the soil characteristic value intervals corresponding to each soil suitability level are integrated into the planting area evaluation standard.

[0093] On the basis of the above embodiments, the index determination module is also used to extract the average annual temperature, annual precipitation and sunshine duration of each candidate planting area from each meteorological data; extract the organic matter content, soil water content and soil mineral content of each candidate planting area from each soil data; match the average annual temperature, annual precipitation and sunshine duration of each candidate planting area with each meteorological characteristic value interval in the planting area evaluation standard, and determine the meteorological suitability level corresponding to each candidate planting area; match the organic matter content, soil water content and soil mineral content of each candidate planting area with each soil characteristic value interval in the planting area evaluation standard, and determine the soil suitability level corresponding to each candidate planting area; based on the preset meteorological factor weights and soil factor weights, the meteorological suitability level and soil suitability level of each candidate planting area are weightedly calculated to obtain the corresponding environmental adaptability index.

[0094] On the basis of the above embodiments, the index determination module is also used to extract corresponding red light band reflectance values ​​and near infrared band reflectance values ​​from each multispectral image; normalize each red light band reflectance value and each near infrared band reflectance value to obtain the vegetation index corresponding to each candidate planting area; determine the vegetation coverage of the corresponding candidate planting area based on the ratio between each vegetation index and the sum of each vegetation index; extract the corresponding terrain elevation value and surface slope value from the multispectral image of each candidate planting area; receive the average annual rainfall of each candidate planting area, and determine the corresponding soil and water loss rate according to the terrain elevation value, surface slope value and average annual rainfall of each candidate planting area.

[0095] Based on the above embodiment, the index determination module is also used to obtain the planting area of ​​each candidate planting area; based on a preset index mapping table, determine the benchmark environmental risk index corresponding to each planting area; multiply the benchmark environmental risk index, vegetation coverage and soil erosion rate of each candidate planting area to obtain the environmental risk index corresponding to each candidate planting area.

[0096] On the basis of the above embodiments, the site selection module is also used to calculate the logistics transportation distance from each candidate planting area to the Chinese medicinal materials processing area based on the location information of the Chinese medicinal materials processing area and the location information of each candidate planting area; determine the logistics cost index corresponding to each candidate planting area based on the preset transportation cost coefficient and each logistics transportation distance; take the ratio of the environmental adaptability index to the environmental risk index of each candidate planting area as the comprehensive environmental index; perform weighted calculation on the comprehensive environmental index and the logistics cost index of each candidate planting area to obtain the site selection priority of each candidate planting area.

[0097] Based on the above embodiment, the site selection module is also used to obtain the Chinese medicine planting density in the target site selection area; when the Chinese medicine planting density exceeds the planting density threshold, an early warning message is generated and sent to the Chinese medicine planting supervision department.

[0098] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0099] The present application also discloses an electronic device. Figure 3 , Figure 3The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0100] The communication bus 302 is used to realize the connection and communication between these components.

[0101] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0102] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0103] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface diagrams and applications, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0104] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a traditional Chinese medicine planting site selection evaluation method based on big data.

[0105] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the memory 305 to store an application program for a method for evaluating the site selection of traditional Chinese medicine planting based on big data. When executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0106] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0107] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0108] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0109] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0110] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0111] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0112] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not recorded in the present disclosure. The description and examples are to be regarded as exemplary only.

Claims

1. A method for evaluating the site selection of traditional Chinese medicine planting based on big data, characterized in that: include: Obtain the categories of Chinese medicinal materials to be planted and soil data and meteorological data of multiple candidate planting areas; Establishing planting area assessment criteria based on plant growth parameters corresponding to the Chinese medicine categories; According to the planting area evaluation standard, the soil data and meteorological data of each candidate planting area are evaluated and processed to obtain a corresponding environmental adaptability index; Receiving multispectral images of each candidate planting area collected by a drone, determining the vegetation coverage and soil erosion rate of each candidate planting area based on each multispectral image, and calculating the corresponding environmental risk index based on the vegetation coverage and soil erosion rate of each candidate planting area; Based on the location information of the Chinese medicinal material processing area and the location information of each of the candidate planting areas, calculating the logistics transportation distance from each of the candidate planting areas to the Chinese medicinal material processing area; Determine the logistics cost index corresponding to each candidate planting area based on the preset transportation cost coefficient and each logistics transportation distance; The ratio of the environmental adaptability index to the environmental risk index of each candidate planting area is used as the comprehensive environmental index; A weighted calculation is performed on the comprehensive environmental index and the logistics cost index of each candidate planting area to obtain the site selection priority of each candidate planting area, and the candidate planting area with the highest site selection priority is used as the target site selection area for the Chinese medicinal materials to be planted.

2. The method for evaluating the site selection of traditional Chinese medicine planting based on big data according to claim 1, characterized in that: The establishment of a planting area assessment standard based on the plant growth parameters corresponding to the traditional Chinese medicine category includes: Determining plant growth parameters corresponding to the Chinese medicine category in a preset plant growth record database, wherein the plant growth parameters include suitable growth meteorological indicators and suitable growth soil indicators; According to the suitable growth meteorological index, a plurality of meteorological characteristic value intervals corresponding to different meteorological suitability levels are divided, and according to the suitable growth soil index, a plurality of soil characteristic value intervals corresponding to different soil suitability levels are divided; The meteorological characteristic value intervals corresponding to the meteorological suitability levels and the soil characteristic value intervals corresponding to the soil suitability levels are integrated into the planting area assessment standard.

3. The method for evaluating the site selection for Chinese medicinal plant planting based on big data according to claim 1, characterized in that: The soil data and meteorological data of each candidate planting area are evaluated and processed according to the planting area evaluation standard to obtain a corresponding environmental adaptability index, including: Extracting the average annual temperature, annual precipitation and sunshine duration of each candidate planting area from each meteorological data; Extracting the organic matter content, soil water content and soil mineral content of each candidate planting area from each soil data; Matching the annual average temperature, annual precipitation and sunshine duration of each candidate planting area with each meteorological characteristic value interval in the planting area evaluation standard to determine the meteorological suitability level corresponding to each candidate planting area; Matching the organic matter content, soil water content and soil mineral content of each candidate planting area with each soil characteristic value interval in the planting area assessment standard, and determining the soil suitability grade corresponding to each candidate planting area; Based on the preset meteorological factor weights and soil factor weights, the meteorological suitability level and soil suitability level of each candidate planting area are weightedly calculated to obtain a corresponding environmental adaptability index.

4. The method for evaluating the site selection for Chinese medicinal plant planting based on big data according to claim 1, characterized in that: The determining of the vegetation coverage and the soil erosion rate of each candidate planting area based on each multispectral image includes: Extracting corresponding red light band reflectance values ​​and near infrared band reflectance values ​​from each of the multispectral images; Normalizing the red light band reflectance values ​​and the near infrared band reflectance values ​​to obtain a vegetation index corresponding to each candidate planting area; Determining the vegetation coverage of the corresponding candidate planting area based on the ratio between each of the vegetation indices and the sum of the vegetation indices; Extracting corresponding terrain elevation values ​​and surface slope values ​​from the multispectral images of each candidate planting area; The average annual rainfall of each candidate planting area is received, and the corresponding soil and water loss rate is determined according to the terrain elevation value, the surface slope value and the average annual rainfall of each candidate planting area.

5. The method for evaluating the site selection for Chinese medicinal herb planting based on big data according to claim 1, characterized in that: The calculating of the corresponding environmental risk index based on the vegetation coverage and soil erosion rate of each candidate planting area includes: Obtaining the planting area of ​​each candidate planting area; Determine the baseline environmental risk index corresponding to each of the planting areas based on a preset index mapping table; The baseline environmental risk index, vegetation coverage and soil erosion rate of each candidate planting area are multiplied to obtain the environmental risk index corresponding to each candidate planting area.

6. The method for evaluating the site selection for Chinese medicinal plant planting based on big data according to claim 1, characterized in that: After selecting the candidate planting area with the highest site selection priority as the target site selection area for the Chinese medicinal materials to be planted, the method further includes: Obtaining the planting density of traditional Chinese medicine in the target site selection area; When the Chinese medicine planting density exceeds the planting density threshold, an early warning message is generated and sent to the Chinese medicine planting supervision department.

7. A Chinese medicine planting site selection evaluation system based on big data, characterized in that: The system comprises: A data acquisition module, used to acquire the categories of Chinese medicinal herbs to be planted and soil data and meteorological data of multiple candidate planting areas; An evaluation standard determination module, used to establish a planting area evaluation standard based on the plant growth parameters corresponding to the traditional Chinese medicine category; An index determination module is used to evaluate and process the soil data and meteorological data of each candidate planting area according to the planting area evaluation standard to obtain a corresponding environmental adaptability index; receive multispectral images of each candidate planting area collected by a drone, determine the vegetation coverage and soil erosion rate of each candidate planting area based on each multispectral image, and calculate the corresponding environmental risk index based on the vegetation coverage and soil erosion rate of each candidate planting area; A site selection module is used to calculate the logistics transportation distance from each candidate planting area to the Chinese medicinal material processing area based on the location information of the Chinese medicinal material processing area and the location information of each candidate planting area; determine the logistics cost index corresponding to each candidate planting area based on a preset transportation cost coefficient and each logistics transportation distance; use the ratio of the environmental adaptability index to the environmental risk index of each candidate planting area as the comprehensive environmental index; perform weighted calculation on the comprehensive environmental index and the logistics cost index of each candidate planting area to obtain the site selection priority of each candidate planting area, and use the candidate planting area with the highest site selection priority as the target site selection area for the Chinese medicinal materials to be planted.

8. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the traditional Chinese medicine planting site selection evaluation method based on big data as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method for evaluating the site selection for Chinese medicinal material planting based on big data as described in any one of claims 1 to 6 is executed.

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