Traditional Chinese medicinal material planting information tracing management system based on Internet of Things

Abstract

The invention discloses a traditional Chinese medicinal material planting information tracing management system based on the Internet of Things, and relates to the technical field of the Internet of Things, the system comprises a management center, and the management center is in communication connection with an information acquisition module, an information management module, a quality tracing module, an intelligent early warning module, a data analysis module and a decision support module, all the modules are connected through electric signals, the information collection module is used for collecting environmental parameters and production data in the traditional Chinese medicine planting process in real time and transmitting the data to the management center, and the information management module is used for integrating the collected environmental parameters and production data. According to the method, the key parameters of the growth environment of the traditional Chinese medicinal materials are collected in real time, and the historical data and the weather forecast information are combined, so that the growth environment of the traditional Chinese medicinal materials can be accurately predicted, scientific planting decision support is provided for planters, personalized environment management strategies are formulated, and planting risks caused by changes of environmental factors are effectively reduced.

Description

Technical Field

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

[0002] In the field of traditional Chinese medicine, the quality of Chinese medicinal materials is directly related to the efficacy of the medicine and the health of the patients. However, the traditional quality control methods of Chinese medicinal materials often have problems such as information opacity and difficulty in traceability. The rapid development of Internet of Things technology has provided technical support for the construction of Chinese medicinal materials planting information traceability management system. Internet of Things technology can collect and transmit various data in the process of Chinese medicinal materials planting in real time through sensors, RFID tags and other equipment, such as soil moisture, temperature, light and other environmental parameters, as well as the growth conditions of Chinese medicinal materials. These data provide the original basis for the quality traceability of Chinese medicinal materials, and also provide a scientific basis for the planting management of Chinese medicinal materials. The widespread application of big data and cloud computing technology enables the Chinese medicinal materials planting information traceability management system to efficiently process and analyze massive data, and can also realize real-time monitoring and recording of various links such as Chinese medicinal materials processing, storage, transportation and sales to ensure that the quality of Chinese medicinal materials meets the standards.

[0003] In the prior art, since the growth of Chinese medicinal materials is affected by a variety of complex and uncertain factors, including climate, soil, pests and diseases, etc., the changes in these factors increase the difficulty of accurately predicting the growth conditions of Chinese medicinal materials, resulting in possible errors in the prediction results. In addition, the growth cycle of Chinese medicinal materials is generally long, and different types of Chinese medicinal materials have great differences in growth cycles, which further increases the risks in the planting process, such as natural disasters, pests and diseases, etc. Therefore, how to predict and evaluate uncertain factors is the problem we need to solve now. Now we propose a Chinese medicinal material planting information traceability management system based on the Internet of Things. Summary of the invention

[0004] The purpose of the present invention is to provide a Chinese medicinal material planting information traceability management system based on the Internet of Things to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: A Chinese herbal medicine planting information traceability management system based on the Internet of Things, comprising a management center, wherein the management center is communicatively connected with an information collection module, an information management module, a quality traceability module, an intelligent early warning module, a data analysis module and a decision support module, wherein electrical signals are connected between the modules; The information collection module is used to collect environmental parameters and production data in the process of planting Chinese medicinal materials in real time, and transmit the data to the management center. The environmental parameters include soil moisture, temperature, light, etc., and the production data include irrigation amount, fertilizer amount, etc.; The information management module is used to integrate the collected environmental parameters and production data, and divide them into real-time data and historical data. At the same time, it pre-processes them and extracts abnormal planting factors, among which the abnormal planting factors include abnormal environmental factors and abnormal pest factors, forming a complete information chain in the process of Chinese medicinal materials planting, ensuring the accuracy and completeness of the data, and providing a reliable basis for the quality traceability of Chinese medicinal materials; The quality traceability module is used to query detailed information on the planting, processing, quality inspection, transportation and sales of Chinese medicinal materials to ensure the quality and safety of Chinese medicinal materials and the traceability of their sources; Intelligent early warning module, used to analyze the real-time data and historical data after preprocessing, and combine the extracted planting abnormal factors to obtain the environmental abnormality index and the pest abnormality index, so as to monitor the abnormal conditions in the growth process of Chinese medicinal materials in real time, analyze the changing trend of the environmental abnormality index and the influence of the pest abnormality index, and issue early warning signals and abnormal information; The data analysis module is used to obtain the planting abnormality assessment coefficient by combining the environmental abnormality index and the pest abnormality index, and determine the different abnormality degrees of the planting conditions by combining the pre-processed historical data, and respectively set the abnormality assessment thresholds to obtain the abnormality assessment sequence table; The decision support module is used to analyze historical data and real-time data, and combined with the planting abnormality assessment coefficient, divide the planting abnormality area, conduct production forecasts, water consumption analysis and fertilizer decisions, dynamically monitor the growth phenotype of Chinese medicinal materials, promptly discover problems in the growth process, and provide a scientific basis for planting decisions.

[0006] A further improvement of the technical solution of the present invention is that the collection process of the environmental parameters and production data is as follows: Deploy various sensors in the Chinese medicinal material planting area, such as temperature sensors, humidity sensors, light sensors, soil moisture sensors, etc., to monitor environmental parameters in the planting area, such as temperature, humidity, wind speed, rainfall, etc.; Surveillance cameras are installed in every corner to monitor the growth status and production process of Chinese medicinal materials as well as the conditions at the planting site, including the weight and location information of the Chinese medicinal materials; Deploy IoT devices to configure wireless networks, mark Chinese medicinal materials during planting, processing and transportation, and transmit them to the management center wirelessly.

[0007] A further improvement of the technical solution of the present invention is that the extraction process of the abnormal planting factor is: Summarize environmental parameters and production data collected from various sensors and surveillance cameras, and perform data cleaning, data conversion and data integration; Extract features from pre-processed historical data, perform feature extraction on environmental conditions and pest conditions, obtain abnormal planting factors, specifically abnormal environmental factors and abnormal pest factors, analyze these features using statistical methods or machine learning algorithms, identify data that exceeds the normal range or shows abnormal change trends, and mark them as abnormal planting factors; According to the pre-processed historical data, the associated data of environmental abnormal factors are obtained, which are temperature data, humidity data, light data and soil moisture data. At the same time, the abnormal factors of pests are obtained, which are irrigation amount data, fertilization amount data and plant growth data. Among them, environmental abnormal factors: by analyzing the environmental parameter data, data points that are significantly different from normal growth conditions are identified, such as abnormally high or low temperatures, too high or too low humidity, too strong or too weak light, etc. These abnormal factors may affect the growth and development of Chinese medicinal materials. Pest abnormal factors: through the data collected by monitoring cameras and other monitoring equipment, signs of pests and diseases are identified, such as leaf damage, abnormal color changes, etc.

[0008] A further improvement of the technical solution of the present invention is that: the quality traceability module includes a planting unit, a harvesting unit, a processing quality inspection unit, a sales unit and an information query unit, wherein each unit is connected by electrical signals; The planting unit is used to record the planting location, planting density, planting time and planting environment of medicinal materials to ensure the accuracy and completeness of planting information, and monitor soil moisture, soil temperature, air temperature, air humidity, light intensity and plant nutrient content, among which the plant nutrient content includes nitrogen, phosphorus and potassium, providing a scientific basis for the growth of Chinese medicinal materials; The harvesting unit is used to record the harvesting time, harvesting location and harvesting quantity to ensure the traceability of the harvesting information, and to conduct quality inspection on the harvested Chinese medicinal materials to ensure that the quality of the Chinese medicinal materials meets the relevant standards; The processing quality inspection unit is used to record the processing procedures, processing time and processing technology to ensure the accuracy and completeness of the processing information, and to conduct quality inspections on the processed Chinese medicinal materials to ensure that the processing quality of the Chinese medicinal materials meets the relevant standards. At the same time, it manages the packaging process of the Chinese medicinal materials to ensure the safety and integrity of the Chinese medicinal materials during transportation and sales; The sales unit is used to record sales time and sales quantity, ensure the traceability of sales information, and manage customer information, including customer purchase records and contact information, to facilitate the sales of Chinese herbal medicines. It also provides management of quality inspection data to ensure that the quality of Chinese herbal medicines during the sales process meets relevant standards; The information query unit is used to set up production management records, harvesting records, processing and packaging records, and transportation and storage related records based on the collected information, and to set up relevant information records of producers and operators, so as to meet the needs of consumers and supervisory departments for tracking information on all links from the query terminal to the production and sales of Chinese medicinal materials, so as to find the relevant persons in charge of the links where problems may occur in the quality of medicinal materials.

[0009] A further improvement of the technical solution of the present invention is that the process of obtaining the environmental abnormality index and the pest abnormality index is as follows: Integrate the associated data of environmental abnormal factors and pest abnormal factors in preprocessed real-time data and historical data to form a unified data set. The data set should include timestamps, environmental parameters, pest parameters, and other possible related factors, such as soil type, Chinese medicinal material type, etc., and build a planting abnormality assessment model; The associated data of environmental abnormal factors and pest abnormal factors in the data set are feature encoded so that the machine learning algorithm can process them, and the integrated data set is divided into training set, validation set and test set. The training set data is used to train the planting abnormality assessment model to learn the relationship between environmental abnormal factors and pest abnormal factors and abnormal growth of Chinese medicinal materials. At the same time, the validation set and test set data are used to verify the trained planting abnormality assessment model and evaluate the performance of the planting abnormality assessment model. Use the trained model and pre-processed historical data to obtain the environmental anomaly index and pest anomaly index, and analyze the deviation between the current environmental parameters and the normal range, as well as the degree to which the current Chinese medicinal materials are threatened by pests. The calculated environmental anomaly index and pest anomaly index are monitored in real time, and the changing trend of the environmental anomaly index is analyzed to understand whether the abnormal situation continues to deteriorate or ease. The impact of the pest anomaly index is analyzed to evaluate the specific impact of pests on the growth of Chinese medicinal materials; including analysis of the relationship between the frequency of pest occurrence, the type of pests and the growth status of Chinese medicinal materials.

[0010] A further improvement of the technical solution of the present invention is that the calculation formula of the environmental anomaly index is: ; Among them, EI is the environmental anomaly index, T is the real-time temperature value, is the standard value of temperature, is the temperature standard deviation, H is the real-time humidity value, is the standard value of humidity, Humidity standard deviation, L is the real-time light intensity value, is the standard value of light intensity, is the standard deviation of light intensity, SW is the real-time soil moisture content, is the standard value of soil moisture, is the standard deviation of soil moisture, k is the time attenuation coefficient, is the time interval since the last measurement; ; Among them, PI is the pest abnormality index, is the current frequency of pest occurrence, is the standard value of the frequency of pest occurrence, is the maximum value of the pest occurrence frequency, is the minimum frequency of pest occurrence, is the maximum number of pest species at present, n is the weight coefficient of the number of pest species, m is the number of pest species monitored, is the severity of the i-th pest, is the standard value of the severity of the i-th pest, is the standard deviation of the severity of the i-th pest, is the change in pest frequency since the last measurement, It is the benchmark value for the frequency of pest occurrence.

[0011] A further improvement of the technical solution of the present invention is that: the process of obtaining the abnormal evaluation sequence table is: Analyze the planting abnormality and the correlation between the environmental abnormality index and the pest abnormality index based on the pre-processed historical data, including environmental parameters such as temperature, humidity, light, soil moisture, etc. and pest occurrence records, as well as the quantitative relationship between the environmental abnormality index and the pest abnormality index, reflecting the importance of environmental factors and pest factors to the growth of Chinese medicinal materials; Different weights are assigned to the environmental abnormality index and the pest abnormality index, and the two indexes are weighted calculated using the assigned weights to obtain the planting abnormality assessment coefficient; According to the pre-processed historical data, the implant abnormality is matched, and according to the size and distribution of the implant abnormality evaluation coefficient, different abnormality levels are determined, namely, the first abnormality level, the second abnormality level and the third abnormality level, wherein the implant abnormality level increases step by step from the first abnormality level to the third abnormality level; Matching the determined different abnormality levels with the results of the planting abnormality assessment coefficient, and setting corresponding abnormality assessment thresholds for the different abnormality levels; Arrange all planting abnormality assessment coefficient values ​​in descending order, assign a corresponding abnormality level to each value, make an abnormality assessment sequence table, list the correspondence between the planting abnormality assessment coefficient and the abnormality level, and analyze the abnormality assessment sequence table to identify the time, frequency and severity of the abnormality, judge the changes in the growth conditions of Chinese medicinal materials, and provide data support for planting management.

[0012] A further improvement of the technical solution of the present invention is that the calculation formula of the planting abnormality assessment coefficient is: ; Among them, DAC is the planting abnormality assessment coefficient, EI is the environmental abnormality index, PI is the pest abnormality index, is the standard value of the environmental anomaly index, is the standard value of the pest abnormality index, is the maximum value of the environmental anomaly index, is the minimum value of the pest abnormality index, is a function of the pest abnormality index.

[0013] A further improvement of the technical solution of the present invention is that: the multiple abnormality levels correspond to multiple abnormality assessment thresholds, wherein the abnormality assessment thresholds include an upper threshold and a lower threshold; The multiple abnormality levels and the multiple abnormality assessment thresholds satisfy the following relationship: Level 1 abnormality ; Second level abnormality ; Level 3 abnormality ; Among them, DAC is the planting abnormality assessment coefficient, A is the upper threshold corresponding to the first-level abnormality level and the lower threshold corresponding to the second-level abnormality level, B is the upper threshold corresponding to the second-level abnormality level and the lower threshold corresponding to the third-level abnormality level, and C is the upper threshold corresponding to the third-level abnormality level, A=0.6, B=0.4, C=0.2.

[0014] A further improvement of the technical solution of the present invention is that the process of dynamically monitoring the growth phenotype of Chinese medicinal materials is: Based on historical data, integrate temperature data, humidity data, light data, soil moisture data, irrigation data, fertilizer data and plant growth data from different sources, analyze images and videos of the growth process of Chinese medicinal materials, monitor the growth status, leaf color and morphological change characteristics of Chinese medicinal materials, such as height, width, number of leaves, etc., and output analysis results and issue early warning signals. Among them, use image processing technology to identify leaf color, judge whether Chinese medicinal materials lack nutrition or suffer from diseases and insect pests, analyze videos in real time, monitor the growth rate and morphological changes of Chinese medicinal materials, and detect abnormal conditions in the growth process of Chinese medicinal materials, such as wilting, diseases and insect pests, etc. through video analysis technology; Based on the analysis results, generate treatment recommendations, including adjusting irrigation and fertilization and improving environmental conditions, and evaluate the effectiveness of measures, conduct a comprehensive analysis of the growth status of Chinese medicinal materials, and predict future growth trends; Based on the planting abnormality assessment coefficient and early warning signal, the planting area is divided into normal area, mild abnormal area, moderate abnormal area and severe abnormal area, and each area is classified into the corresponding abnormal level. At the same time, the abnormal situation in the abnormal area is recorded, including the time, location, type and index value of the abnormality, and the treatment suggestions, implementation of measures and treatment results are recorded. At the same time, the growth status and environmental data of Chinese medicinal materials are continuously monitored; Generate reports on the planting environment and production data of Chinese medicinal materials regularly, including statistics on abnormal events and evaluation of treatment effects; the reports can be presented in the form of charts, tables, text, etc., which are convenient for relevant personnel to view and analyze. The report content includes: Environmental data: displaying historical and real-time data of environmental parameters such as temperature, humidity, light and soil moisture; Growth data: displaying the growth data of Chinese medicinal materials, including growth rate, leaf color and morphological changes; Abnormal events: listing all abnormal events, including time, location, type and treatment measures; Effect evaluation: evaluating the effects of measures taken and providing suggestions for improvement. At the same time, a feedback mechanism is established to continuously improve monitoring and analysis methods based on feedback from growers and managers.

[0015] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art: The present invention provides a Chinese medicinal material planting information traceability management system based on the Internet of Things. By real-time collection and recording of data on the entire process from planting to harvesting of Chinese medicinal materials, the transparency of the planting process is significantly improved. The system accurately records key information such as environmental parameters, irrigation and fertilization conditions, pest and disease prevention and control measures, and growth cycles and morphological changes of Chinese medicinal materials for each planting site, making the planting process of Chinese medicinal materials traceable, and the source, planting method and quality information of Chinese medicinal materials can be easily queried, thereby providing strong support for the quality supervision of Chinese medicinal materials. In addition, once a quality problem occurs, the system can quickly locate the source of the problem, making it easy to take measures in a timely manner to reduce losses.

[0016] The present invention provides a Chinese medicinal material planting information traceability management system based on the Internet of Things. By integrating and analyzing historical data and real-time monitoring data, scientific decision-making support is provided for Chinese medicinal material planting. The system intelligently recommends optimal management measures such as irrigation, fertilization, and pest and disease control according to the growth needs of Chinese medicinal materials and current environmental conditions, thereby optimizing planting strategies and improving the yield and quality of Chinese medicinal materials. At the same time, the system can also monitor the growth status of Chinese medicinal materials, promptly discover abnormal conditions in the growth process and issue early warning signals, reminding growers to take corresponding measures, effectively preventing or reducing losses, and improving the planting efficiency of Chinese medicinal materials.

[0017] The present invention provides a Chinese medicinal material planting information traceability management system based on the Internet of Things. Through remote monitoring and control, the system can intelligently adjust irrigation and fertilization plans according to the growth needs and environmental conditions of the Chinese medicinal materials, thereby avoiding ineffective investment of resources and further improving the economic benefits of the Chinese medicinal materials. In addition, the system improves the yield and quality of the Chinese medicinal materials by optimizing the planting strategy.

[0018] The present invention provides a Chinese medicinal material planting information traceability management system based on the Internet of Things. By real-time collection of key parameters of the Chinese medicinal material growth environment and combining historical data and weather forecast information, it is possible to achieve accurate prediction of the Chinese medicinal material growth environment, thereby providing scientific planting decision support for growers and formulating personalized environmental management strategies to effectively reduce the planting risks caused by changes in environmental factors. Such accurate environmental predictions and personalized management strategies not only improve the success rate of Chinese medicinal material planting, but also significantly reduce the economic losses caused by natural disasters, pests and diseases, and other problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0020] Figure 1 The module diagram of the present invention; Figure 2 The flowchart of obtaining the abnormal environment index and the abnormal pest index of the present invention; Figure 3 The present invention is a flowchart for obtaining the planting abnormality assessment coefficient. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Embodiment 1, as Figure 1-3 As shown, the present invention provides a Chinese herbal medicine planting information traceability management system based on the Internet of Things, including a management center, the management center is communicatively connected with an information collection module, an information management module, a quality traceability module, an intelligent early warning module, a data analysis module and a decision support module, wherein the modules are connected by electrical signals; The information collection module is used to collect environmental parameters and production data in the process of Chinese medicinal materials planting in real time, and transmit the data to the management center, wherein the environmental parameters include soil moisture, temperature, light, etc., and the production data include irrigation amount, fertilization amount, etc. The process of collecting the environmental parameters and production data is as follows: deploy various sensors in the Chinese medicinal materials planting area, such as temperature sensors, humidity sensors, light sensors, soil moisture sensors, etc., to monitor the environmental parameters of the planting area, such as temperature, humidity, wind speed, rainfall, etc., and install monitoring cameras in various corners to monitor the growth status and production process of Chinese medicinal materials and the situation at the planting site, including the weight and location information of Chinese medicinal materials, deploy Internet of Things devices to configure wireless networks, mark the Chinese medicinal materials during planting, processing and transportation, and transmit them to the management center wirelessly; The information management module is used to integrate the collected environmental parameters and production data, and divide them into real-time data and historical data, and pre-process them to extract abnormal planting factors, wherein the abnormal planting factors include abnormal environmental factors and abnormal pest factors, so as to form a complete information chain in the process of Chinese medicinal materials planting, ensure the accuracy and completeness of the data, and provide a reliable basis for the quality traceability of Chinese medicinal materials; the extraction process of the abnormal planting factors is: summarizing the environmental parameters and production data collected from various sensors and monitoring cameras, and performing data cleaning, data conversion and data integration; wherein data cleaning includes removing duplicate, invalid or abnormal data, for example, removing or correcting the temperature and humidity values ​​that exceed the reasonable range; for missing data, interpolation method, mean method and other methods are used to fill in the missing data; data conversion includes converting data of different sources, formats and structures into a unified standard format for subsequent processing and analysis, for example, converting parameters such as temperature and humidity into a unified unit of measurement; converting image data into numerical values ​​or feature vectors that can be used for analysis; data integration includes integrating the cleaned and converted data according to time sequence, data type, etc. to form a complete data set. This data set will contain environmental parameters, production data and the correlation information between them. Features will be extracted from the pre-processed historical data. Feature extraction will be performed for environmental conditions and pest conditions respectively to obtain abnormal planting factors, specifically environmental abnormal factors and pest abnormal factors. Statistical methods or machine learning algorithms will be used to analyze these features, identify data that exceeds the normal range or shows abnormal change trends, and mark them as abnormal planting factors. Based on the pre-processed historical data, the associated data of environmental abnormal factors will be obtained, which are temperature data, humidity data, light data and soil moisture data. At the same time, pest abnormal factors will be obtained, which are irrigation data, fertilization data and plant growth data. Environmental abnormal factors: by analyzing environmental parameter data, data points that are significantly different from normal growth conditions are identified, such as abnormal high or low temperatures, excessive or low humidity, excessive or weak light, etc. These abnormal factors may affect the growth and development of Chinese medicinal materials. Pest abnormal factors: data collected through surveillance cameras and other monitoring equipment can be used to identify signs of pests and diseases, such as leaf damage and abnormal color changes. The quality traceability module is used to query detailed information on the planting, processing, quality inspection, transportation and sales of Chinese medicinal materials to ensure the quality safety and traceability of the sources of Chinese medicinal materials; the quality traceability module includes a planting unit, a harvesting unit, a processing quality inspection unit, a sales unit and an information query unit, wherein each unit is connected by electrical signals; the planting unit is used to record the planting address, planting density, planting time and planting environment of the medicinal materials to ensure the accuracy and completeness of the planting information, and monitor soil moisture, soil temperature, air temperature, air humidity, light intensity and plant nutrient content, wherein the plant nutrient content includes nitrogen, phosphorus and potassium, providing a scientific basis for the growth of Chinese medicinal materials; the harvesting unit is used to record the harvesting time, harvesting location and harvesting amount to ensure the traceability of the harvesting information, and to conduct quality inspection on the harvested Chinese medicinal materials to ensure that the quality of the Chinese medicinal materials meets the relevant standards; the processing quality inspection unit is used to record the processing procedures, processing time and processing work The system is used to ensure the accuracy and completeness of processing information, and to conduct quality inspection on processed Chinese medicinal materials to ensure that the processing quality of Chinese medicinal materials meets the relevant standards. At the same time, it manages the packaging process of Chinese medicinal materials to ensure the safety and integrity of Chinese medicinal materials during transportation and sales. The sales unit is used to record sales time and sales quantity, ensure the traceability of sales information, and manage customer information, including customer purchase records and contact information, to facilitate the sales of Chinese medicinal materials, and provide management of quality inspection data to ensure that the quality of Chinese medicinal materials during the sales process meets the relevant standards. The information query unit is used to set production management records, harvesting records, processing and packaging records, and transportation and storage records based on the collected information, and set relevant information records of producers and operators to meet the needs of consumers and supervisory departments for information tracking of all links from the query terminal to the production and sales of Chinese medicinal materials, so as to query the relevant persons in charge of the links where problems may occur in the quality of medicinal materials; The intelligent early warning module is used to analyze the pre-processed real-time data and historical data, and in combination with the extracted planting abnormality factors, obtain the environmental abnormality index and the pest abnormality index, so as to monitor the abnormal conditions in the growth process of Chinese medicinal materials in real time, analyze the changing trend of the environmental abnormality index and the influence of the pest abnormality index, and issue early warning signals and abnormal information; the acquisition process of the environmental abnormality index and the pest abnormality index is: integrating the associated data of the environmental abnormality factors and the pest abnormality factors in the pre-processed real-time data and historical data to form a unified data set, the data set should contain timestamps, environmental parameters, pest parameters and possible other related factors, such as soil type, Chinese medicinal material type, etc., and construct a planting abnormality assessment model, feature encode the associated data of the environmental abnormality factors and the pest abnormality factors in the data set so that the machine learning algorithm can process it, including converting categorical variables into numerical variables, such as using unique hot encoding or label encoding, and standardizing or normalizing numerical variables, and converting the integrated The data set is divided into training set, validation set and test set. The training set data is used to train the planting abnormality assessment model to learn the relationship between environmental abnormality factors and pest abnormality factors and abnormal growth of Chinese medicinal materials. At the same time, the data of the validation set and the test set are used to verify the trained planting abnormality assessment model and evaluate the performance of the planting abnormality assessment model. The evaluation indicators include accuracy, recall rate, F1 score, AUC-ROC curve, etc. The trained model is used in combination with the pre-processed historical data to obtain the environmental abnormality index and the pest abnormality index. The degree of deviation between the current environmental parameters and the normal range and the degree to which the current Chinese medicinal materials are threatened by pests are analyzed. The calculated environmental abnormality index and pest abnormality index are monitored in real time, and the changing trend of the environmental abnormality index is analyzed to understand whether the abnormal situation continues to deteriorate or is alleviated. The degree of influence of the pest abnormality index is analyzed to evaluate the specific impact of pests on the growth of Chinese medicinal materials; including analyzing the relationship between the frequency of pests, the types of pests and the growth status of Chinese medicinal materials; The data analysis module is used to obtain the planting abnormality assessment coefficient by combining the environmental abnormality index and the pest abnormality index, and determine the different abnormal degrees of the planting conditions by combining the pre-processed historical data, and respectively set the abnormality assessment thresholds to obtain the abnormality assessment sequence table; the acquisition process of the abnormality assessment sequence table is as follows: According to the pre-processed historical data, the correlation between the abnormal planting situation and the environmental abnormality index and the pest abnormality index is analyzed, including environmental parameters such as temperature, humidity, light, soil moisture, etc. and pest occurrence records, as well as the quantitative relationship between the environmental abnormality index and the pest abnormality index, reflecting the importance of environmental factors and pest factors to the growth of Chinese medicinal materials; different weights are assigned to the environmental abnormality index and the pest abnormality index, and the two indexes are weightedly calculated using the assigned weights to obtain the planting abnormality evaluation coefficient, the planting abnormal situation is matched according to the pre-processed historical data, and different abnormality levels are determined according to the size and distribution of the planting abnormality evaluation coefficient, which are the first abnormality level, Secondary abnormality level and tertiary abnormality level, where the abnormality level of planting abnormality increases from the primary abnormality level to the tertiary abnormality level, the determined different abnormality levels are matched with the results of the planting abnormality assessment coefficient, corresponding abnormality assessment thresholds are set for different abnormality levels, all planting abnormality assessment coefficient values ​​are arranged in order from high to low, and a corresponding abnormality level is assigned to each value, an abnormality assessment sequence table is made, the correspondence between the planting abnormality assessment coefficient and the abnormality level is listed, and the abnormality assessment sequence table is analyzed to identify the time, frequency and severity of the abnormality, judge the changes in the growth conditions of Chinese medicinal materials, and provide data support for planting management; The decision support module is used to analyze historical data and real-time data, and in combination with the planting abnormality assessment coefficient, divide the planting abnormality area, make production forecasts, water consumption analysis and fertilizer decisions, dynamically monitor the growth phenotype of Chinese medicinal materials, and promptly discover problems in the growth process to provide a scientific basis for planting decisions; the process of dynamically monitoring the growth phenotype of Chinese medicinal materials is as follows: Based on historical data, temperature data, humidity data, light data, soil moisture data, irrigation data, fertilizer data and plant growth data from different sources are integrated, and images and videos of the growth process of Chinese medicinal materials are analyzed to monitor the growth status, leaf color and morphological change characteristics of Chinese medicinal materials, such as height, width, number of leaves, etc., and output the analysis results and issue early warning signals. Among them, image processing technology is used to identify leaf color to determine whether Chinese medicinal materials are lacking in nutrition or suffering from pests and diseases. Real-time analysis of videos is performed to monitor the growth rate and morphological changes of Chinese medicinal materials. Abnormal conditions in the growth process of Chinese medicinal materials, such as wilting, pests and diseases, are detected through video analysis technology. According to the analysis results, treatment suggestions are generated, including adjusting irrigation amount, fertilizer amount and improving environmental conditions, and evaluating the effectiveness of measures. A comprehensive analysis is conducted on the growth status of Chinese medicinal materials to predict future growth trends. Based on the planting abnormality assessment coefficient and early warning signal, the planting area is divided into normal area, slightly abnormal area and moderately abnormal area. The system identifies abnormal conditions in abnormal areas, including the time, location, type and index value of the abnormality, and records the treatment suggestions, implementation of measures and treatment results. At the same time, it continuously monitors the growth status and environmental data of Chinese medicinal materials, and regularly generates reports on the planting environment and production data of Chinese medicinal materials, including statistics on abnormal events and evaluation of treatment effects. The reports can be presented in the form of charts, tables, text, etc., which are convenient for relevant personnel to view and analyze. The report contents include: Environmental data: displaying historical and real-time data of environmental parameters such as temperature, humidity, light and soil moisture; Growth data: displaying the growth data of Chinese medicinal materials, including growth rate, leaf color and morphological changes; Abnormal events: listing all abnormal events, including time, location, type and treatment measures; Effect evaluation: evaluating the effects of measures taken and providing suggestions for improvement. At the same time, it establishes a feedback mechanism to continuously improve monitoring and analysis methods based on feedback from growers and managers.

[0023] Embodiment 2, as Figure 1-3 As shown, based on Example 1, the present invention provides a technical solution: Preferably, the calculation formula of the environmental anomaly index is: ; Among them, EI is the environmental anomaly index, T is the real-time temperature value, is the standard value of temperature, is the temperature standard deviation, H is the real-time humidity value, is the standard value of humidity, Humidity standard deviation, L is the real-time light intensity value, is the standard value of light intensity, is the standard deviation of light intensity, SW is the real-time soil moisture content, is the standard value of soil moisture, is the standard deviation of soil moisture, k is the time attenuation coefficient, is the time interval since the last measurement; ; Among them, PI is the pest abnormality index, is the current frequency of pest occurrence, is the standard value of the frequency of pest occurrence, is the maximum value of the pest occurrence frequency, is the minimum frequency of pest occurrence, is the maximum number of pest species at present, n is the weight coefficient of the number of pest species, m is the number of pest species monitored, is the severity of the i-th pest, is the standard value of the severity of the i-th pest, is the standard deviation of the severity of the i-th pest, is the change in pest frequency since the last measurement, is the benchmark value of pest occurrence frequency; The calculation formula of the implant abnormality assessment coefficient is: ; Among them, DAC is the planting abnormality assessment coefficient, EI is the environmental abnormality index, PI is the pest abnormality index, is the standard value of the environmental anomaly index, is the standard value of the pest abnormality index, is the maximum value of the environmental anomaly index, is the minimum value of the pest abnormality index, is a function of the pest abnormality index; The plurality of abnormality levels correspond to the plurality of abnormality assessment thresholds, wherein the abnormality assessment thresholds include an upper threshold and a lower threshold; The multiple abnormality levels and the multiple abnormality assessment thresholds satisfy the following relationship: Level 1 abnormality ; Second level abnormality ; Level 3 abnormality ; Among them, DAC is the planting abnormality assessment coefficient, A is the upper threshold corresponding to the first-level abnormality level and the lower threshold corresponding to the second-level abnormality level, B is the upper threshold corresponding to the second-level abnormality level and the lower threshold corresponding to the third-level abnormality level, and C is the upper threshold corresponding to the third-level abnormality level, A=0.6, B=0.4, C=0.2.

[0024] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A Chinese herbal medicine planting information traceability management system based on the Internet of Things, including a management center, characterized in that: The management center is communicatively connected with an information collection module, an information management module, a quality tracing module, an intelligent early warning module, a data analysis module and a decision support module, wherein electrical signals are connected between the modules; The information collection module is used to collect environmental parameters and production data during the planting process of Chinese medicinal materials in real time, and transmit the data to the management center; The information management module is used to integrate the collected environmental parameters and production data, and divide them into real-time data and historical data, and pre-process them to extract abnormal planting factors, wherein the abnormal planting factors include abnormal environmental factors and abnormal pest factors; The quality traceability module is used to query detailed information on the planting, processing, quality inspection, transportation and sales of Chinese medicinal materials; The intelligent early warning module is used to analyze the real-time data and historical data after preprocessing, and obtain the environmental abnormality index and the pest abnormality index in combination with the extracted planting abnormality factors, so as to monitor the abnormal conditions in the growth process of Chinese medicinal materials in real time, analyze the changing trend of the environmental abnormality index and the influence degree of the pest abnormality index, and issue early warning signals and abnormal information. The process of obtaining the environmental abnormality index and the pest abnormality index is as follows: Integrate the correlation data of environmental abnormal factors and pest abnormal factors in preprocessed real-time data and historical data to form a unified data set, and build a planting abnormality assessment model; The associated data of environmental abnormal factors and pest abnormal factors in the data set are feature encoded, and the integrated data set is divided into training set, validation set and test set. The training set data is used to train the planting abnormality assessment model, and the validation set and test set data are used to verify the trained planting abnormality assessment model, and the performance of the planting abnormality assessment model is evaluated; Use the trained model and pre-processed historical data to obtain the environmental anomaly index and pest anomaly index, and analyze the deviation between the current environmental parameters and the normal range, as well as the degree to which the current Chinese medicinal materials are threatened by pests. Real-time monitoring of the calculated environmental abnormality index and pest abnormality index is carried out to analyze the changing trend of the environmental abnormality index and the impact of the pest abnormality index, so as to evaluate the specific impact of pests on the growth of Chinese medicinal materials; The calculation formula of the environmental anomaly index is: ； Among them, EI is the environmental anomaly index, T is the real-time temperature value, is the standard value of temperature, is the temperature standard deviation, H is the real-time humidity value, is the standard value of humidity, Humidity standard deviation, L is the real-time light intensity value, is the standard value of light intensity, is the standard deviation of light intensity, SW is the real-time soil moisture content, is the standard value of soil moisture, is the standard deviation of soil moisture, k is the time attenuation coefficient, is the time interval since the last measurement; ； Among them, PI is the pest abnormality index, is the current frequency of pest occurrence, is the standard value of the frequency of pest occurrence, is the maximum value of the pest occurrence frequency, is the minimum frequency of pest occurrence, is the maximum number of pest species at present, n is the weight coefficient of the number of pest species, m is the number of pest species monitored, is the severity of the i-th pest, is the standard value of the severity of the i-th pest, is the standard deviation of the severity of the i-th pest, is the change in pest frequency since the last measurement, is the benchmark value of pest occurrence frequency; The data analysis module is used to obtain a planting abnormality assessment coefficient by combining the environmental abnormality index and the pest abnormality index, and determine different abnormal degrees of the planting conditions by combining the pre-processed historical data, and respectively set abnormality assessment thresholds to obtain an abnormality assessment sequence table; The decision support module is used to analyze historical data and real-time data, and in combination with the planting abnormality assessment coefficient, divide the planting abnormality area, perform production forecasting, water consumption analysis and fertilizer decision-making, and dynamically monitor the growth phenotype of Chinese medicinal materials.

2. According to the Internet of Things-based Chinese herbal medicine planting information traceability management system according to claim 1, it is characterized by: The collection process of the environmental parameters and production data is as follows: Deploy various sensors in the Chinese medicinal material planting area to monitor the environmental parameters of the planting area; Surveillance cameras are installed in every corner to monitor the growth status and production process of Chinese medicinal materials as well as the conditions at the planting site, including the weight and location information of the Chinese medicinal materials; Deploy IoT devices to configure wireless networks, mark Chinese medicinal materials during planting, processing and transportation, and transmit them to the management center wirelessly.

3. According to the Internet of Things-based Chinese herbal medicine planting information traceability management system according to claim 2, it is characterized by: The extraction process of the abnormal implantation factor is as follows: Summarize environmental parameters and production data collected from various sensors and surveillance cameras, and perform data cleaning, data conversion and data integration; Extract features from pre-processed historical data, perform feature extraction on environmental conditions and pest conditions, and obtain abnormal planting factors, specifically, abnormal environmental factors and abnormal pest factors; According to the pre-processed historical data, the associated data of environmental abnormal factors are obtained, which are temperature data, humidity data, light data and soil moisture data. At the same time, the abnormal factors of pests are obtained, which are irrigation amount data, fertilizer amount data and plant growth data.

4. According to claim 3, a Chinese medicinal material planting information traceability management system based on the Internet of Things is characterized by: The quality traceability module includes a planting unit, a harvesting unit, a processing quality inspection unit, a sales unit, and an information query unit, wherein each unit is connected by electrical signals; The planting unit is used to record the planting location, planting density, planting time and planting environment of the medicinal materials, and monitor soil moisture, soil temperature, air temperature, air humidity, light intensity and plant nutrient content, wherein the plant nutrient content includes nitrogen, phosphorus and potassium; The harvesting unit is used to record the harvesting time, harvesting location and harvesting amount, and to perform quality inspection on the harvested Chinese medicinal materials; The processing quality inspection unit is used to record the processing procedures, processing time and processing technology, and to perform quality inspection on the processed Chinese medicinal materials, while managing the packaging process of the Chinese medicinal materials; The sales unit is used to record sales time and sales quantity, and manage customer information, including customer purchase records and contact information, and provide quality inspection data management; The information query unit is used to set production management records, harvesting records, processing and packaging records, and transportation and storage related records according to the collected information, and to set producer and operator related information records.

5. According to the Internet of Things-based Chinese herbal medicine planting information traceability management system of claim 4, it is characterized by: The process of obtaining the abnormal evaluation sequence table: Analyze the planting abnormality and the correlation between the environmental abnormality index and the pest abnormality index based on the pre-processed historical data, as well as the quantitative relationship between the environmental abnormality index and the pest abnormality index; Different weights are assigned to the environmental abnormality index and the pest abnormality index, and the two indexes are weighted calculated using the assigned weights to obtain the planting abnormality assessment coefficient; According to the pre-processed historical data, the implant abnormality is matched, and according to the size and distribution of the implant abnormality evaluation coefficient, different abnormality levels are determined, namely, the first abnormality level, the second abnormality level and the third abnormality level, wherein the implant abnormality level increases step by step from the first abnormality level to the third abnormality level; Matching the determined different abnormality levels with the results of the planting abnormality assessment coefficient, and setting corresponding abnormality assessment thresholds for the different abnormality levels; Arrange all planting abnormality assessment coefficient values ​​in descending order, assign a corresponding abnormality level to each value, make an abnormality assessment sequence table, list the correspondence between the planting abnormality assessment coefficient and the abnormality level, and analyze the abnormality assessment sequence table to identify the time, frequency and severity of the abnormality, and judge the changes in the growth conditions of Chinese medicinal materials.

6. According to the Internet of Things-based Chinese herbal medicine planting information traceability management system of claim 5, it is characterized by: The calculation formula of the implant abnormality assessment coefficient is: ； Among them, DAC is the planting abnormality assessment coefficient, EI is the environmental abnormality index, PI is the pest abnormality index, is the standard value of the environmental anomaly index, is the standard value of the pest abnormality index, is the maximum value of the environmental anomaly index, is the minimum value of the pest abnormality index, is a function of the pest abnormality index.

7. The Chinese medicinal material planting information traceability management system based on the Internet of Things according to claim 6 is characterized by: The plurality of abnormality levels correspond to the plurality of abnormality assessment thresholds, wherein the abnormality assessment thresholds include an upper threshold and a lower threshold; The multiple abnormality levels and the multiple abnormality assessment thresholds satisfy the following relationship: Level 1 abnormality ; Second level abnormality ; Level 3 abnormality ; Among them, DAC is the planting abnormality assessment coefficient, A is the upper threshold corresponding to the first-level abnormality level and the lower threshold corresponding to the second-level abnormality level, B is the upper threshold corresponding to the second-level abnormality level and the lower threshold corresponding to the third-level abnormality level, and C is the upper threshold corresponding to the third-level abnormality level, A=0.6, B=0.4, C=0.

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8. The Chinese herbal medicine planting information traceability management system based on the Internet of Things according to claim 7, characterized in that: The process of dynamically monitoring the growth phenotype of Chinese medicinal materials is as follows: Based on historical data, it integrates temperature data, humidity data, light data, soil moisture data, irrigation data, fertilizer data, and plant growth data from different sources, and analyzes images and videos of the growth process of Chinese medicinal materials to monitor the growth status, leaf color, and morphological change characteristics of Chinese medicinal materials. It also outputs analysis results and issues early warning signals. Based on the analysis results, generate treatment recommendations, including adjusting irrigation and fertilization and improving environmental conditions, and evaluate the effectiveness of measures, conduct a comprehensive analysis of the growth status of Chinese medicinal materials, and predict future growth trends; Based on the planting abnormality assessment coefficient and early warning signal, the planting area is divided into normal area, mild abnormal area, moderate abnormal area and severe abnormal area, and each area is classified into the corresponding abnormal level. At the same time, the abnormal situation in the abnormal area is recorded, including the time, location, type and index value of the abnormality, and the treatment suggestions, implementation of measures and treatment results are recorded; Regularly generate reports on Chinese medicinal materials planting environment and production data, including statistics on abnormal events and evaluation of treatment effects.

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