Intelligent agricultural monitoring intelligent management system based on data analysis

Abstract

The invention discloses an intelligent agricultural monitoring intelligent management system based on data analysis, and relates to the technical field of intelligent agricultural management. Crop data and air environment data are acquired in real time through an integrated sensor group and acquisition equipment installed in a farmland, and are transmitted to a terminal processing module through a wireless network LoRa for preprocessing; generating a crop disease resistance data set, and storing the crop disease resistance data set in a data storage library; the method comprises the following steps: calculating a pathogen spore concentration index MND, carrying out preliminary evaluation on the MND and a preset pathogen spore concentration threshold A, calculating a crop comprehensive health index ZJK after judging that crops are free of diseases, carrying out secondary evaluation on the ZJK and a preset crop health threshold B, and displaying a monitoring result and personalized management suggestions by using data visualization software Tableau. And farmers are helped to adjust management strategies in time, so that precise agricultural management is realized, and the disease resistance and production efficiency of crops are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart agricultural management, and in particular to a smart agricultural monitoring intelligent management system based on data analysis. Background Art

[0002] With the continued growth of the global population and the agricultural challenges brought about by climate change, smart agriculture has become the key to promoting sustainable development. Modern agriculture is facing problems such as pests and diseases, environmental changes and resource shortages, and urgently needs efficient management and monitoring systems to improve crop yields and quality. Advances in data analysis technology have made real-time monitoring and intelligent decision-making possible, providing farmers with accurate management suggestions, thereby optimizing resource allocation and reducing pesticide use to ensure sustainable agricultural development.

[0003] At present, many traditional agricultural management methods still rely on experience and lack scientific data support, resulting in insufficient monitoring of crop disease resistance. The lack of real-time data analysis and dynamic feedback makes it difficult for farmers to understand disease risks in a timely manner, which may lead to large-scale damage to crops and cause economic losses. In addition, existing monitoring methods often ignore the impact of environmental changes on crop health and make it difficult to provide personalized management recommendations. These current situations are mainly due to the lag in agricultural management models and insufficient application of technology, resulting in limitations in data collection and analysis. This situation not only causes a waste of resources, but may also trigger outbreaks of diseases, further affecting crop yields and quality, increasing the economic burden on farmers, and causing potential harm to the ecological environment. Therefore, a systematic solution is urgently needed to achieve more scientific and precise agricultural management. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a smart agricultural monitoring intelligent management system based on data analysis, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a smart agricultural monitoring and intelligent management system based on data analysis, including a data acquisition module, a terminal processing module, a pathogen concentration analysis module, a crop resistance analysis module, a comprehensive health analysis module and a real-time feedback module;

[0006] The data acquisition module collects crop data and air environment data in real time through the installed integrated sensor group and acquisition equipment, and communicates with the integrated sensor group and acquisition equipment through the wireless network LoRa to transmit the crop data and air environment data to the terminal processing module in real time;

[0007] The terminal processing module is used to receive crop data and air environment data in real time, and perform preprocessing to obtain a crop disease resistance data group, and then store the crop disease resistance data group in a data storage library;

[0008] The pathogen concentration analysis module extracts the pathogen spore data group stored in the data repository through the database port, and performs summary calculation to obtain the pathogen spore concentration index MND, and then performs a preliminary evaluation with the preset pathogen spore concentration threshold A, and triggers a secondary evaluation mechanism based on the evaluation result;

[0009] The crop resistance analysis module is used to extract the biochemical resistance data group and the physiological resistance data group stored in the data storage repository through the database port according to the preliminary assessment of the crop being disease-free, and to perform summary calculation to obtain the crop biochemical resistance index HXZ and the crop physiological resistance index SLK;

[0010] The comprehensive health analysis module is used to obtain the pathogen spore concentration index MND, the crop biochemical resistance index HXZ and the crop physiological resistance index SLK for summary calculation, obtain the crop comprehensive health index ZJK, and conduct a secondary evaluation with the preset crop health threshold B, deeply evaluate the disease resistance of the crop, and generate relevant early warning information according to the evaluation results;

[0011] The real-time feedback module is used to generate a real-time monitoring dashboard using the data visualization software Tableau for the initial and secondary evaluations, and transmit the dashboard to the user client via the wireless network LoRa to generate personalized crop management recommendations.

[0012] Preferably, the data acquisition module includes a data acquisition unit and a data transmission unit;

[0013] The data collection unit collects crop data and air environment data in real time by setting monitoring points at different locations in the crop field and installing an integrated sensor group and collection equipment in each monitoring point; the integrated sensor group includes an air velocity sensor, a temperature sensor, a humidity sensor and a biosensor; the collection equipment includes a spectrophotometer, a stomatal conductance meter, a spectrum analyzer and an ultrasonic thickness meter;

[0014] The pathogen spore content in the air is analyzed in near real time by combining biosensors with PCR technology;

[0015] The air flow rate ll is collected in real time through the air flow rate sensor;

[0016] The ambient humidity sd is collected in real time through the humidity sensor;

[0017] The ambient temperature wd is collected in real time through the temperature sensor;

[0018] The survival time t of pathogen spores is obtained through experiments under specific temperature and humidity conditions and can be used directly;

[0019] The phenolic content fw and lignin content mz were collected in real time by a spectrometer;

[0020] The peroxidase inhibitor content gy is collected in real time by a biosensor;

[0021] The blade thickness hd is obtained by measuring with an ultrasonic thickness gauge;

[0022] Stomatal conductance qk is measured and obtained by a stomatal conductance measuring instrument;

[0023] The antioxidant enzyme activity ky was measured by spectrophotometer;

[0024] The data transmission unit is used to communicate with the terminal processing module through the wireless network LoRa, and transmit the collected crop data and air environment data to the terminal processing module in real time through the CSMA transmission protocol.

[0025] Preferably, the terminal processing module includes a data preprocessing unit and a data storage unit;

[0026] The data preprocessing unit is used to receive crop data and air environment data in real time, and perform noise removal, missing value filling, verification, filtering and normalization processing to obtain a crop disease resistance data group;

[0027] The crop disease resistance data set includes a pathogen spore data set, a biochemical resistance data set and a physiological resistance data set;

[0028] The pathogen spore data set includes the pathogen spore content sl in the air, the air flow rate ll, the ambient humidity sd, the ambient temperature wd and the pathogen spore survival time t;

[0029] The biochemical resistance data set includes phenolic content fw, peroxidase inhibitor content gy and lignin content mz;

[0030] The physiological resistance data set includes leaf thickness hd, stomatal conductance qk and antioxidant enzyme activity ky;

[0031] The data storage unit is used to store the preprocessed crop disease resistance data group in a data storage repository through an API port, and to extract data of the crop disease resistance data group in the data storage repository in real time through a database port.

[0032] Preferably, the pathogen concentration analysis module includes a pathogen spore concentration calculation unit and a pathogen spore concentration analysis unit;

[0033] The pathogen spore concentration calculation unit extracts the pathogen spore data group stored in the data repository through the database port, and performs summary calculation to obtain the pathogen spore concentration index MND;

[0034] The pathogen spore concentration index MND is calculated by the following formula:

[0035]

[0036] In the formula, a represents the correction constant, sd 0 Indicates the suitable environmental humidity for pathogen spores sd, wd 0 It indicates that the pathogen spores are suitable for the environmental temperature wd, k1 indicates the humidity influence coefficient, k2 indicates the temperature influence coefficient, k3 indicates the time influence coefficient, and e indicates the exponential function.

[0037] Preferably, the pathogen spore concentration analysis unit accesses the average value of the historical pathogen spore concentration in the data repository, presets the pathogen spore concentration threshold A, and performs a preliminary evaluation with the obtained pathogen spore concentration index MND. The specific evaluation scheme is as follows;

[0038] When the pathogen spore concentration index MND ≥ the pathogen spore concentration threshold A, it indicates that a crop disease has broken out, and at this time, a first warning message is generated, and the first warning message is transmitted to the user client via a wireless network to remind the user to take disease prevention measures;

[0039] When the pathogen spore concentration index MND is less than the pathogen spore concentration threshold A, it indicates that the crops are disease-free. At this time, normal monitoring is maintained and the second evaluation mechanism is triggered.

[0040] Preferably, the crop resistance analysis module is used to access the crop disease resistance data group in the data storage library based on the preliminary assessment that the crop is disease-free, and extract the biochemical resistance data group and the physiological resistance data group through the database port;

[0041] The crop resistance analysis module includes a crop biochemical resistance calculation unit and a crop physiological resistance calculation unit;

[0042] The crop biochemical resistance calculation unit is used to perform summary calculation based on the acquired biochemical resistance data group to obtain the crop biochemical resistance index HXZ;

[0043] The crop biochemical resistance index HXZ is calculated by the following formula:

[0044]

[0045] Where b1 represents the adjustment coefficient.

[0046] Preferably, the crop physiological resistance calculation unit is used to perform summary calculation based on the acquired physiological resistance data group to obtain the crop physiological resistance index SLK;

[0047] The crop physiological resistance index SLK is calculated by the following formula:

[0048]

[0049] Where b2 represents the adjustment coefficient.

[0050] Preferably, the comprehensive health analysis module includes a crop health analysis unit and a crop health assessment unit;

[0051] The crop health analysis unit is used to perform summary calculation based on the obtained pathogen spore concentration index MND, crop biochemical resistance index HXZ and crop physiological resistance index SLK to obtain a comprehensive crop health index ZJK;

[0052] The comprehensive crop health index ZJK is calculated by the following formula:

[0053] ZJK=w1*MND+w2*HXZ+w3*SLK;

[0054] Wherein, w1, w2 and w3 represent the weight coefficients of the pathogen spore concentration index MND, the crop biochemical resistance index HXZ and the crop physiological resistance index SLK, respectively, and w1+w2+w3=1, and the specific values ​​are set by the user.

[0055] Preferably, the crop health assessment unit is used to preset the crop health threshold B, and conduct a secondary comparative assessment with the obtained crop comprehensive health index ZJK, deeply assess the disease resistance of the crops, and generate corresponding early warning information based on the assessment results. The specific assessment scheme is as follows;

[0056] When the comprehensive crop health index ZJK ≥ the crop health threshold B, it means that the crops are healthy, disease resistance is qualified, and normal monitoring is maintained;

[0057] When the comprehensive crop health index ZJK is less than the crop health threshold B, it means that the crops are unhealthy and their disease resistance is unqualified. At this time, a second warning message is generated and transmitted to the user client through the wireless network LoRa, reminding the user to take management measures for the crops, including fertilization, irrigation and disease prevention measures, and increase the push frequency of the client's real-time crop situation by 50%.

[0058] Preferably, the real-time feedback module is used to generate a real-time monitoring dashboard using the data visualization software Tableau for the health assessment results of the pathogen concentration analysis module and the comprehensive health analysis module, and transmit it to the user client via the wireless network LoRa to display the crop health status, early warning information and historical data analysis; combined with machine learning algorithms, historical data and current indicators are automatically analyzed to generate personalized crop management recommendations, including fertilization, irrigation or disease control measures. Users can receive push notifications through mobile applications to respond and adjust crop management strategies immediately.

[0059] The present invention provides a smart agricultural monitoring and intelligent management system based on data analysis. It has the following beneficial effects:

[0060] (1) The data acquisition module of the system uses the installed integrated sensors and acquisition equipment to collect data on crops and air environment in real time. It provides accurate basic data for the system, so that subsequent analysis and decision-making are based on reliable information. The terminal processing module removes noise, verifies and filters the data to ensure the accuracy and integrity of the data, and stores the processed data in the data repository for access by subsequent analysis modules.

[0061] (2) The system's pathogen concentration analysis module and crop resistance analysis module are combined to deeply evaluate the health status of crops. By calculating the pathogen spore concentration index MND, the system can identify potential disease risks and generate early warning information. When the pathogen spore concentration index MND exceeds the preset pathogen spore concentration threshold A, the system will immediately trigger an alarm to prompt the user to take protective measures; when the pathogen spore concentration index MND is lower than the preset pathogen spore concentration threshold A, the secondary assessment mechanism is triggered. This rapid response mechanism can effectively control the spread of diseases in the early stages and reduce the risk of losses.

[0062] (3) The system's comprehensive health analysis module calculates the crop comprehensive health index ZJK by summarizing the pathogen spore concentration index MND, the crop biochemical resistance index HXZ, and the crop physiological resistance index SLK. The crop comprehensive health index ZJK helps users fully understand the health status of crops and conduct secondary assessments based on the preset crop health threshold B. Through data visualization software, the system displays these analysis results to users in real time to support intuitive decision-making. At the same time, combined with machine learning algorithms, the system can analyze historical data and current indicators to provide personalized management recommendations, such as fertilization, irrigation, and disease control strategies. Ultimately, this system not only improves the efficiency of agricultural management, but also provides strong support for achieving precision agriculture and sustainable development goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1This is a flow chart of a smart agricultural monitoring intelligent management system based on data analysis according to the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Example 1

[0066] See also Figure 1 , the present invention provides a smart agricultural monitoring intelligent management system based on data analysis. To achieve the above purpose, the present invention is implemented through the following technical solutions: including a data acquisition module, a terminal processing module, a pathogen concentration analysis module, a crop resistance analysis module, a comprehensive health analysis module and a real-time feedback module;

[0067] The data acquisition module collects crop data and air environment data in real time through the installed integrated sensor group and acquisition equipment, and communicates with the integrated sensor group and acquisition equipment through the wireless network LoRa to transmit the crop data and air environment data to the terminal processing module in real time;

[0068] The terminal processing module is used to receive crop data and air environment data in real time, and perform preprocessing to obtain a crop disease resistance data group, and then store the crop disease resistance data group in a data storage library and extract it through a database port;

[0069] The pathogen concentration analysis module extracts the pathogen spore data group stored in the data repository through the database port, and performs summary calculation to obtain the pathogen spore concentration index MND, and then performs a preliminary evaluation with the preset pathogen spore concentration threshold A, and triggers a secondary evaluation mechanism based on the evaluation result;

[0070] The crop resistance analysis module is used to extract the biochemical resistance data group and the physiological resistance data group stored in the data storage repository through the database port according to the preliminary assessment of the crop being disease-free, and to perform summary calculation to obtain the crop biochemical resistance index HXZ and the crop physiological resistance index SLK;

[0071] The comprehensive health analysis module is used to obtain the pathogen spore concentration index MND, the crop biochemical resistance index HXZ and the crop physiological resistance index SLK for summary calculation, obtain the crop comprehensive health index ZJK, and conduct a secondary evaluation with the preset crop health threshold B, deeply evaluate the disease resistance of the crop, and generate relevant early warning information according to the evaluation results;

[0072] The real-time feedback module is used to generate a real-time monitoring dashboard using the data visualization software Tableau for the initial and secondary evaluations, and transmit the dashboard to the user client via the wireless network LoRa to generate personalized crop management recommendations.

[0073] In this embodiment, the data acquisition module uses an integrated sensor group and acquisition equipment to obtain data on crops and air environment in real time, and communicates with the integrated sensor group and acquisition equipment through the wireless network LoRa, and transmits the crop data and air environment data to the terminal processing module in real time. This ensures the immediacy and accuracy of the data and provides a solid foundation for subsequent analysis. The terminal processing module is responsible for preprocessing these data to ensure the integrity and reliability of the information, thereby laying the foundation for the effective operation of the pathogen concentration analysis and crop resistance analysis modules. The combination of the pathogen concentration analysis module and the crop resistance analysis module realizes an in-depth assessment of the health status of crops. By calculating the pathogen spore concentration index MND, a preliminary assessment is made with the preset pathogen spore concentration threshold A, and based on the assessment results, a secondary assessment mechanism is triggered, and with the crop biochemical resistance index HXZ and the crop physiological resistance index SLK, the disease risk can be quickly identified and early warning information can be generated. Compared with traditional means, this real-time monitoring and analysis mechanism greatly shortens the response time, enabling farmers to take protective measures in the early stages of disease outbreaks, thereby effectively reducing potential losses. The comprehensive health analysis module calculates the comprehensive crop health index ZJK by summarizing the pathogen spore concentration index MND, the crop biochemical resistance index HXZ and the crop physiological resistance index SLK, and performs a secondary evaluation with the preset crop health threshold B. This process not only improves the accuracy of crop management, but also realizes data visualization, helping users to intuitively understand the health status of crops. At the same time, combined with machine learning technology, the system can automatically generate personalized management recommendations, such as fertilization and irrigation strategies, to ensure that crops grow under optimal conditions. Overall, the system represents a leap forward in agricultural management technology, promotes the goals of sustainable development and precision agriculture, and achieves a significant improvement in management efficiency.

[0074] Example 2

[0075] This embodiment is explained in Example 1, please refer to Figure 1,Specifically: the data acquisition module includes a data acquisition unit and a data transmission unit;

[0076] The data collection unit collects crop data and air environment data in real time by setting monitoring points at different locations in the crop field and installing an integrated sensor group and collection equipment in each monitoring point; the integrated sensor group includes an air velocity sensor, a temperature sensor, a humidity sensor and a biosensor; the collection equipment includes a spectrophotometer, a stomatal conductance meter, a spectrum analyzer and an ultrasonic thickness meter;

[0077] The data transmission unit is used to communicate with the terminal processing module through the wireless network LoRa, and transmit the collected crop data and air environment data to the terminal processing module in real time through the CSMA transmission protocol.

[0078] In this embodiment, the design of the data acquisition module realizes comprehensive real-time monitoring of crop and air environment data by integrating a variety of sensors and acquisition devices. The diversity of the integrated sensor group enables the system to accurately obtain key parameters such as air velocity, temperature, humidity and biological indicators, thereby providing rich basic data for crop health assessment. At the same time, by using an efficient data transmission unit, the connection with the terminal processing module through the wireless network LoRa ensures that the data is transmitted in real time with low latency and high reliability. This efficient data acquisition and transmission method not only improves the accuracy of information acquisition, but also greatly reduces the time cost of traditional manual monitoring, greatly enhances the real-time and flexibility of agricultural management, and lays a solid foundation for the implementation of precision agriculture, helping farmers make timely and scientific decisions in a rapidly changing environment.

[0079] Example 3

[0080] This embodiment is explained in Example 2. Please refer to Figure 1 ,Specifically: the terminal processing module includes a data preprocessing unit and a data storage unit;

[0081] The data preprocessing unit is used to receive crop data and air environment data in real time, and perform noise removal, missing value filling, verification, filtering and normalization processing to obtain a crop disease resistance data group;

[0082] The crop disease resistance data set includes a pathogen spore data set, a biochemical resistance data set and a physiological resistance data set;

[0083] The pathogen spore data set includes the pathogen spore content sl in the air, the air flow rate ll, the ambient humidity sd, the ambient temperature wd and the pathogen spore survival time t;

[0084] The biochemical resistance data set includes phenolic content fw, peroxidase inhibitor content gy and lignin content mz;

[0085] The physiological resistance data set includes leaf thickness hd, stomatal conductance qk and antioxidant enzyme activity ky;

[0086] The data storage unit is used to store the preprocessed crop disease resistance data group in a data storage repository through an API port, and to extract data of the crop disease resistance data group in the data storage repository in real time through a database port.

[0087] In this embodiment, the terminal processing module provides important support and advantages for crop management through the efficient operation of the data preprocessing unit and the data storage unit. The data preprocessing unit ensures the accuracy and completeness of the crop disease resistance data set by removing noise, filling missing values ​​and normalizing, making subsequent analysis more reliable. This module can effectively integrate pathogen spore data sets, biochemical resistance data sets and physiological resistance data sets, thereby comprehensively reflecting the health status of crops. The data storage unit promotes seamless connection between various analysis modules by extracting and storing processed data in real time, ensuring the timeliness and consistency of data. This highly integrated data processing capability not only improves the scientificity and effectiveness of decision-making, but also provides strong support for precision agriculture, enabling farmers to respond to potential disease risks in a timely manner, optimize crop management strategies, and ultimately achieve higher yields and quality.

[0088] Example 4

[0089] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: the pathogen concentration analysis module includes a pathogen spore concentration calculation unit and a pathogen spore concentration analysis unit;

[0090] The pathogen spore concentration calculation unit extracts the pathogen spore data group stored in the data repository through the database port, and performs summary calculation to obtain the pathogen spore concentration index MND;

[0091] The pathogen spore concentration index MND is calculated by the following formula:

[0092]

[0093] In the formula, a represents the correction constant, sd 0 Indicates the suitable environmental humidity for pathogen spores sd, wd 0 It indicates that the pathogen spores are suitable for the environmental temperature wd, k1 indicates the humidity influence coefficient, k2 indicates the temperature influence coefficient, k3 indicates the time influence coefficient, and e indicates the exponential function.

[0094] The pathogen spore concentration analysis unit accesses the average value of the historical pathogen spore concentration in the data repository, performs a preset pathogen spore concentration threshold A, and performs a preliminary evaluation with the obtained pathogen spore concentration index MND. The specific evaluation scheme is as follows;

[0095] When the pathogen spore concentration index MND ≥ the pathogen spore concentration threshold A, it indicates that a crop disease has broken out, and at this time, a first warning message is generated, and the first warning message is transmitted to the user client via a wireless network to remind the user to take disease prevention measures;

[0096] When the pathogen spore concentration index MND is less than the pathogen spore concentration threshold A, it indicates that the crops are disease-free. At this time, normal monitoring is maintained and the second evaluation mechanism is triggered.

[0097] In this embodiment, the pathogen concentration analysis module greatly improves the scientificity and efficiency of agricultural disease prevention and control through the close cooperation of the pathogen spore concentration calculation unit and the analysis unit. By accurately calculating the pathogen spore concentration index MND and comparing it with the preset pathogen spore concentration threshold A, the system can issue an early warning in time before the disease breaks out, helping farmers to take protective measures quickly. This early warning mechanism not only reduces the economic losses caused by diseases, but also effectively reduces the use of pesticides and promotes the implementation of sustainable agriculture. In addition, the real-time monitoring function of the system ensures normal management in a disease-free state, thereby maintaining crop health and improving overall crop yield and quality. This module demonstrates its unique practical value and social benefits in disease prevention, drug saving and increasing crop output.

[0098] Example 5

[0099] This embodiment is explained in Example 4. Please refer to Figure 1 ,Specifically: the crop resistance analysis module is used to access the crop disease resistance data group in the data repository when the crops are preliminarily evaluated to be disease-free, and extract the biochemical resistance data group and the physiological resistance data group through the database port;

[0100] The crop resistance analysis module includes a crop biochemical resistance calculation unit and a crop physiological resistance calculation unit;

[0101] The crop biochemical resistance calculation unit is used to perform summary calculation based on the acquired biochemical resistance data group to obtain the crop biochemical resistance index HXZ;

[0102] The crop biochemical resistance index HXZ is calculated by the following formula:

[0103]

[0104] Where b1 represents the adjustment coefficient.

[0105] The crop physiological resistance calculation unit is used to perform summary calculation based on the acquired physiological resistance data group to obtain the crop physiological resistance index SLK;

[0106] The crop physiological resistance index SLK is calculated by the following formula:

[0107]

[0108] Where b2 represents the adjustment coefficient.

[0109] In this embodiment, the crop resistance analysis module provides an accurate resistance assessment tool for agricultural management by extracting and calculating the crop biochemical resistance index HXZ and the crop physiological resistance index SLK. The design of this module can not only deeply analyze the resistance characteristics of crops when the crops are initially assessed to be disease-free, but also help farmers understand the adaptability and disease resistance of crops under different environmental conditions through quantitative indexation. Such precise analysis enables farmers to formulate more targeted management strategies, such as optimizing fertilization and irrigation plans, thereby improving the disease resistance and growth potential of crops. In addition, based on the dynamically adjusted calculation formula, the system can flexibly adapt to different crop varieties and growth environments, further improving management efficiency and yield, and laying a solid foundation for sustainable agricultural practices.

[0110] Example 6

[0111] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the comprehensive health analysis module includes a crop health analysis unit and a crop health assessment unit;

[0112] The crop health analysis unit is used to perform summary calculation based on the obtained pathogen spore concentration index MND, crop biochemical resistance index HXZ and crop physiological resistance index SLK to obtain a comprehensive crop health index ZJK;

[0113] The comprehensive crop health index ZJK is calculated by the following formula:

[0114] ZJK=w1*MND+w2*HXZ+w3*SLK;

[0115] Wherein, w1, w2 and w3 represent the weight coefficients of the pathogen spore concentration index MND, the crop biochemical resistance index HXZ and the crop physiological resistance index SLK, respectively, and w1+w2+w3=1, and the specific values ​​are set by the user.

[0116] The crop health assessment unit is used to preset the crop health threshold B, and conduct a secondary comparative assessment with the obtained crop comprehensive health index ZJK, deeply assess the disease resistance of the crops, and generate corresponding early warning information based on the assessment results. The specific assessment scheme is as follows;

[0117] When the comprehensive crop health index ZJK ≥ the crop health threshold B, it means that the crops are healthy, disease resistance is qualified, and normal monitoring is maintained;

[0118] When the comprehensive crop health index ZJK is less than the crop health threshold B, it means that the crops are unhealthy and their disease resistance is unqualified. At this time, a second warning message is generated and transmitted to the user client through the wireless network LoRa, reminding the user to take management measures for the crops, including fertilization, irrigation and disease prevention measures, and increase the push frequency of the client's real-time crop situation by 50%.

[0119] In this embodiment, the comprehensive health analysis module significantly improves the accuracy and responsiveness of crop management by calculating the comprehensive crop health index ZJK and comparing and evaluating it with the preset crop health threshold B. When the comprehensive crop health index ZJK is higher than the preset crop health threshold B, the system confirms that the crop is healthy, maintains routine monitoring, avoids unnecessary intervention, and saves resources; when the comprehensive crop health index ZJK is lower than the preset crop health threshold B, an early warning message is immediately generated to remind the user to take measures in time, thereby effectively preventing the spread of diseases. This intelligent health assessment mechanism not only improves the real-time monitoring of crop disease resistance, but also can flexibly adjust management strategies according to actual health status, ultimately promoting efficient production and sustainable development of crops, and ensuring that farmers have stronger coping capabilities and decision-making support when facing uncertainty.

[0120] Example 7

[0121] This embodiment is explained in Example 1, please refer to Figure 1 Specifically: the real-time feedback module is used to generate a real-time monitoring dashboard using the data visualization software Tableau based on the health assessment results of the pathogen concentration analysis module and the comprehensive health analysis module, and transmit it to the user client through the wireless network LoRa to display the crop health status, early warning information and historical data analysis; combined with machine learning algorithms, historical data and current indicators are automatically analyzed to generate personalized crop management recommendations. Users can receive push notifications through mobile applications to respond and adjust crop management strategies immediately.

[0122] In this embodiment, the real-time monitoring dashboard generated by the data visualization software Tableau greatly improves the intuitiveness and operability of crop management. This module not only displays the health status and early warning information of crops, but also transmits important data to the user client in real time through the wireless network LoRa, so that farmers can keep abreast of the latest developments of crops at any time. Combined with machine learning algorithms, it can automatically analyze historical data and current indicators, generate personalized management suggestions, and help users accurately respond to different crop needs. Through the push notification function of the mobile application, farmers can respond and adjust management strategies immediately, optimize the growth environment and protective measures of crops, thereby significantly improving the efficiency and flexibility of agricultural management and promoting more scientific and precise agricultural practices.

[0123] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart agricultural monitoring and intelligent management system based on data analysis, characterized by: It includes data collection module, terminal processing module, pathogen concentration analysis module, crop resistance analysis module, comprehensive health analysis module and real-time feedback module; The data acquisition module collects crop data and air environment data in real time through the installed integrated sensor group and acquisition equipment, and communicates with the integrated sensor group and acquisition equipment through the wireless network LoRa to transmit the crop data and air environment data to the terminal processing module in real time; The terminal processing module is used to receive crop data and air environment data in real time, and perform preprocessing to obtain a crop disease resistance data group, and then store the crop disease resistance data group in a data storage library; The pathogen concentration analysis module extracts the pathogen spore data group stored in the data repository through the database port, and performs summary calculation to obtain the pathogen spore concentration index MND, and then performs a preliminary evaluation with the preset pathogen spore concentration threshold A, and triggers a secondary evaluation mechanism based on the evaluation result; The crop resistance analysis module is used to extract the biochemical resistance data group and the physiological resistance data group stored in the data storage repository through the database port according to the preliminary assessment of the crop being disease-free, and to perform summary calculation to obtain the crop biochemical resistance index HXZ and the crop physiological resistance index SLK; The comprehensive health analysis module is used to obtain the pathogen spore concentration index MND, the crop biochemical resistance index HXZ and the crop physiological resistance index SLK for summary calculation, obtain the crop comprehensive health index ZJK, and conduct a secondary evaluation with the preset crop health threshold B, deeply evaluate the disease resistance of the crop, and generate relevant early warning information according to the evaluation results; The real-time feedback module is used to generate a real-time monitoring dashboard using the data visualization software Tableau for the initial and secondary evaluations, and transmit the dashboard to the user client via the wireless network LoRa to generate personalized crop management recommendations.

2. According to claim 1, a smart agricultural monitoring intelligent management system based on data analysis is characterized in that: The data acquisition module includes a data acquisition unit and a data transmission unit; The data collection unit collects crop data and air environment data in real time by setting monitoring points at different locations in the crop field and installing an integrated sensor group and collection equipment in each monitoring point; the integrated sensor group includes an air velocity sensor, a temperature sensor, a humidity sensor and a biosensor; the collection equipment includes a spectrophotometer, a stomatal conductance meter, a spectrum analyzer and an ultrasonic thickness meter; The data transmission unit is used to communicate with the terminal processing module through the wireless network LoRa, and transmit the collected crop data and air environment data to the terminal processing module in real time through the CSMA transmission protocol.

3. According to claim 2, a smart agricultural monitoring intelligent management system based on data analysis is characterized in that: The terminal processing module includes a data preprocessing unit and a data storage unit; The data preprocessing unit is used to receive crop data and air environment data in real time, and perform noise removal, missing value filling, verification, filtering and normalization processing to obtain a crop disease resistance data group; The crop disease resistance data set includes a pathogen spore data set, a biochemical resistance data set and a physiological resistance data set; The pathogen spore data set includes the pathogen spore content sl in the air, the air flow rate ll, the ambient humidity sd, the ambient temperature wd and the pathogen spore survival time t; The biochemical resistance data set includes phenolic content fw, peroxidase inhibitor content gy and lignin content mz; The physiological resistance data set includes leaf thickness hd, stomatal conductance qk and antioxidant enzyme activity ky; The data storage unit is used to store the preprocessed crop disease resistance data group in a data storage repository through an API port, and to extract the crop disease resistance data group in the data storage repository in real time through a database port.

4. According to claim 3, a smart agricultural monitoring intelligent management system based on data analysis is characterized in that: The pathogen concentration analysis module includes a pathogen spore concentration calculation unit and a pathogen spore concentration analysis unit; The pathogen spore concentration calculation unit extracts the pathogen spore data group stored in the data repository through the database port, and performs summary calculation to obtain the pathogen spore concentration index MND; The pathogen spore concentration index MND is calculated by the following formula: In the formula, a represents the correction constant, sd0 represents the suitable environmental humidity sd for pathogen spores, wd0 represents the suitable environmental temperature wd for pathogen spores, k1 represents the humidity influence coefficient, k2 represents the temperature influence coefficient, k3 represents the time influence coefficient, and e represents the exponential function.

5. According to claim 4, a smart agricultural monitoring intelligent management system based on data analysis is characterized in that: The pathogen spore concentration analysis unit accesses the average value of the historical pathogen spore concentration in the data repository, performs a preset pathogen spore concentration threshold A, and performs a preliminary evaluation with the obtained pathogen spore concentration index MND. The specific evaluation scheme is as follows; When the pathogen spore concentration index MND ≥ the pathogen spore concentration threshold A, it indicates that a crop disease has broken out, and at this time, a first warning message is generated, and the first warning message is transmitted to the user client via a wireless network to remind the user to take disease prevention measures; When the pathogen spore concentration index MND is less than the pathogen spore concentration threshold A, it indicates that the crops are disease-free. At this time, normal monitoring is maintained and the second evaluation mechanism is triggered.

6. According to claim 5, a smart agricultural monitoring intelligent management system based on data analysis is characterized in that: The crop resistance analysis module is used to access the crop disease resistance data group in the data storage library based on the preliminary assessment that the crop is disease-free, and extract the biochemical resistance data group and the physiological resistance data group through the database port; The crop resistance analysis module includes a crop biochemical resistance calculation unit and a crop physiological resistance calculation unit; The crop biochemical resistance calculation unit is used to perform summary calculation based on the acquired biochemical resistance data group to obtain the crop biochemical resistance index HXZ; The crop biochemical resistance index HXZ is calculated by the following formula: Where b1 represents the adjustment coefficient.

7. The smart agricultural monitoring and intelligent management system based on data analysis according to claim 6 is characterized by: The crop physiological resistance calculation unit is used to perform summary calculation based on the acquired physiological resistance data group to obtain the crop physiological resistance index SLK; The crop physiological resistance index SLK is calculated by the following formula: Where b2 represents the adjustment coefficient.

8. The smart agricultural monitoring and intelligent management system based on data analysis according to claim 1 is characterized by: The comprehensive health analysis module includes a crop health analysis unit and a crop health assessment unit; The crop health analysis unit is used to perform summary calculation based on the obtained pathogen spore concentration index MND, crop biochemical resistance index HXZ and crop physiological resistance index SLK to obtain a comprehensive crop health index ZJK; The comprehensive crop health index ZJK is calculated by the following formula: ZJK=w1*MND+w2*HXZ+w3*SLK; Wherein, w1, w2 and w3 represent the weight coefficients of the pathogen spore concentration index MND, the crop biochemical resistance index HXZ and the crop physiological resistance index SLK, respectively, and w1+w2+w3=1, and the specific values ​​are set by the user.

9. The smart agricultural monitoring and intelligent management system based on data analysis according to claim 8 is characterized by: The crop health assessment unit is used to preset the crop health threshold B, and conduct a secondary comparative assessment with the obtained crop comprehensive health index ZJK, deeply assess the disease resistance of the crops, and generate corresponding early warning information based on the assessment results. The specific assessment scheme is as follows; When the comprehensive crop health index ZJK ≥ the crop health threshold B, it means that the crops are healthy, disease resistance is qualified, and normal monitoring is maintained; When the comprehensive crop health index ZJK is less than the crop health threshold B, it means that the crops are unhealthy and their disease resistance is unqualified. At this time, a second warning message is generated and transmitted to the user client through the wireless network LoRa, reminding the user to take management measures for the crops, including fertilization, irrigation and disease prevention measures, and increase the frequency of real-time crop situation push on the client by 50%.

10. The smart agricultural monitoring and intelligent management system based on data analysis according to claim 1 is characterized in that: The real-time feedback module is used to generate a real-time monitoring dashboard using the data visualization software Tableau based on the health assessment results of the pathogen concentration analysis module and the comprehensive health analysis module, and transmit it to the user client via the wireless network LoRa to display the crop health status, early warning information and historical data analysis. It combines machine learning algorithms to automatically analyze historical data and current indicators to generate personalized crop management recommendations. Users can receive push notifications through mobile applications to respond and adjust crop management strategies immediately.

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