An agricultural smart management system based on the Internet of Things

Abstract

The present invention discloses an agricultural intelligent management system based on the Internet of Things, which relates to the field of agricultural management technology. Through the efficient collaboration of the irrigation data acquisition module and the intelligent irrigation algorithm module, the system can comprehensively collect soil data, meteorological data and crop irrigation factors related to irrigation, and generate intelligent irrigation feature vectors through feature extraction and processing modules, and finally calculate the precise irrigation demand IrrigAmt. During the irrigation process, the system not only takes into account the real-time environmental data, but also makes dynamic adjustments based on the historical mean to calculate the optimal comprehensive irrigation coefficient IrrigFinal to ensure that each irrigation can achieve the expected effect. This intelligent irrigation algorithm based on the combination of real-time and historical data greatly improves the utilization rate of water resources, reduces the waste of resources caused by over-irrigation or insufficient irrigation, and finally achieves the goal of precision agriculture.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural management, and in particular to an agricultural intelligent management system based on the Internet of Things. Background Art

[0002] IoT technology has been widely used in various fields, especially in the agricultural field, where it has brought significant intelligent and automated changes to agricultural production. Specifically, smart agriculture combines IoT technology with agricultural production to achieve real-time monitoring and automated management of the crop growth environment. Among them, the IoT-based agricultural smart management system provides precise irrigation management solutions for agricultural production by collecting multi-dimensional data such as soil moisture, temperature, and meteorological conditions. The core of these systems is the optimization of intelligent irrigation algorithms, which calculate the optimal irrigation requirements of crops by analyzing and processing large amounts of real-time data to achieve efficient use of water resources.

[0003] At present, many current agricultural smart management systems still have some shortcomings in practical applications. For example, in traditional irrigation management, the system often relies on preset irrigation schedules or simple soil moisture thresholds, and lacks the ability to dynamically respond to real-time environmental changes. This approach may lead to waste of water resources or failure to provide sufficient water when crops need it, ultimately affecting crop growth and yield. In addition, existing irrigation algorithms are usually too simple to effectively integrate multiple influencing factors such as weather changes, soil type, and crop growth stage, resulting in insufficient accuracy of irrigation decisions and failure to meet the requirements of precision agriculture. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides an agricultural smart management system based on the Internet of Things, which solves the problems mentioned in the background technology.

[0006] Technical Solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: including an irrigation data acquisition module, a feature extraction and processing module, an intelligent irrigation algorithm module, a result summary and execution module and a user interface and operation module;

[0008] The irrigation data collection module collects environmental data related to irrigation by installing monitoring equipment and Internet of Things integrated equipment, and summarizes and generates an environmental data group;

[0009] The feature extraction module is used to extract features from the collected environmental data group and generate intelligent irrigation feature vectors for algorithm optimization;

[0010] The intelligent irrigation algorithm module is used to calculate the irrigation demand IrrigAmt according to the generated irrigation characteristic vector, and calculate the adjustment demand IrrigAdj according to the irrigation demand IrrigAmt, and then comprehensively calculate the obtained irrigation demand IrrigAmt and the adjustment demand IrrigAdj to obtain the comprehensive irrigation coefficient IrrigFinal;

[0011] The summary and execution module is used to calculate the effective irrigation coefficient EffIrrig based on the irrigation feature vector, and preset the irrigation effect threshold G, perform comparative evaluation, and determine the next irrigation strategy;

[0012] The user interface and operation module are used to provide users with visualized irrigation decisions and results, and provide manual adjustment options.

[0013] Preferably, the irrigation data collection module collects soil data of the irrigation land in real time by installing detection equipment around the agricultural irrigation land, and then constructs an API application program interface to integrate with the Internet of Things meteorological platform to collect meteorological data of the irrigation land in real time;

[0014] The detection equipment includes a humidity sensor and a temperature sensor;

[0015] The soil data include soil moisture SD and soil temperature WD;

[0016] The meteorological data include humidity Qsd, temperature Qwd, precipitation Qjs and wind speed Qfs;

[0017] At the same time, the planting density Zmd and crop water requirement Zxs are set based on agricultural crops;

[0018] Then the acquired soil data, meteorological data and crop irrigation factor CRP are summarized into an environmental data group;

[0019] The data from the sensors is then collected through the IoT gateway and transmitted to the feature extraction and processing module.

[0020] Preferably, the feature extraction and processing module includes a feature extraction unit and a preprocessing unit;

[0021] The feature extraction unit is used to extract soil irrigation parameters SMT, meteorological parameters WEA and crop parameters CRP based on soil data and meteorological data in the environmental data group;

[0022] The soil irrigation parameter SMT is calculated and extracted through the soil moisture SD and soil temperature WD in the soil data. The calculation formula is: ;

[0023] The meteorological parameter WEA is calculated and extracted through the humidity Qsd, temperature Qwd, precipitation Qjs and wind speed Qfs in the meteorological data. The calculation formula is: ;

[0024] The crop parameter CRP is calculated and extracted by the set planting density Zmd and crop water requirement Zxs. The calculation formula is: ;

[0025] The preprocessing unit is used to standardize the extracted intelligent irrigation feature vector to eliminate the influence of different dimensions, and then combine the soil irrigation parameter SMT, the meteorological parameter WEA and the crop parameter CRP to generate the intelligent irrigation feature vector.

[0026] Preferably, the intelligent irrigation algorithm module includes an irrigation demand analysis unit, a dynamic adjustment unit and a comprehensive control unit;

[0027] The irrigation demand analysis unit is used to calculate the irrigation demand IrrigAmt of agricultural crops according to the obtained irrigation characteristic vector;

[0028] The irrigation demand IrrigAmt is calculated by the following algorithm formula:

[0029] ;

[0030] Where k1, k2 and k3 are the preset weight values ​​of soil irrigation parameter SMT, meteorological parameter WEA and crop parameter CRP respectively.

[0031] Preferably, the adjustment unit is used to dynamically adapt to environmental changes by introducing a comparison between current conditions and historical averages based on the obtained irrigation demand IrrigAmt, and to calculate and obtain the adjusted demand IrrigAdj;

[0032] The adjustment demand IrrigAdj is calculated and obtained by the following algorithm formula:

[0033] ;

[0034] Where SMTavg represents the historical average soil irrigation parameter, WEAavg represents the historical average meteorological parameter, △SMT represents the difference between the current soil irrigation parameter SMT and the historical average soil irrigation parameter SMT, and △WEA represents the difference between the current meteorological parameter WEA and the historical average meteorological parameter WEA.

[0035] Preferably, the comprehensive control unit is used to integrate the irrigation demand IrrigAmt and the adjustment demand IrrigAdj, and calculate to obtain the comprehensive irrigation coefficient IrrigFinal;

[0036] The comprehensive irrigation coefficient IrrigFinal is calculated and obtained by the following algorithm formula;

[0037] ;

[0038] Where PrevIrrig represents the data of the last irrigation. , and They respectively represent the irrigation demand IrrigAmt, the adjustment demand IrrigAdj and the adjustment coefficient of the previous irrigation data PrevIrrig.

[0039] Preferably, the result summary and execution module includes an irrigation effect analysis unit and an execution unit;

[0040] The irrigation effect analysis unit is used to analyze the irrigation effect of each time and calculate and obtain the effective irrigation coefficient EffIrrig;

[0041] ;

[0042] Where n represents the total number of data points, Abs represents the absolute value function, IrrigFinal,i represents the actual comprehensive irrigation coefficient of the i-th data point, IrrigFinal2,i represents the target comprehensive irrigation coefficient of the i-th data point, and Wi represents the weight value of the i-th data point;

[0043] The execution unit is used to preset the irrigation effect threshold G based on the crop irrigation index, and then compare and evaluate the irrigation strategy to be executed next time with the effective irrigation coefficient EffIrrig. The specific evaluation content is as follows;

[0044] When the effective irrigation coefficient EffIrrig is less than the irrigation effect threshold G, it means that the irrigation effect is normal and the current strategy continues to be implemented;

[0045] When the effective irrigation coefficient EffIrrig ≥ irrigation effect threshold G, it means that the irrigation effect is abnormal, and the weight values ​​k1, k2 and k3 and the adjustment coefficient , and An increase of 10% is made and a reminder is generated to the staff to adjust the irrigation strategy.

[0046] Preferably, the user interface and operation module is used to develop a user interface using modern front-end frameworks such as React and Vue.js, support dynamic data updates and interactive operations, and display the dynamic change curves of the comprehensive irrigation coefficient IrrigFinal and the effective irrigation coefficient EffIrrig in the same user interface, and provide sliders, input boxes and knob interactive elements on the user interface, allowing users to manually adjust the weights k1, k2 and k3 and the adjustment coefficients. , and After each adjustment, the system will immediately update the calculation results of the dynamic change curve of the comprehensive irrigation coefficient IrrigFinal and the effective irrigation coefficient EffIrrig, and display the change results in real time on the interface.

[0047] A computer device comprises: a memory and a processor; the memory stores a computer program, and when the processor executes the computer program, an agricultural smart management system based on the Internet of Things is implemented for monitoring the actual effect of the irrigation process and providing instant feedback.

[0048] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements an agricultural intelligence management system based on the Internet of Things.

[0049] Beneficial Effects

[0050] The present invention provides an agricultural intelligent management system based on the Internet of Things. It has the following beneficial effects:

[0051] (1) Through the efficient collaboration of the irrigation data acquisition module and the intelligent irrigation algorithm module, the system can comprehensively collect soil data, meteorological data and crop irrigation factors related to irrigation, and generate intelligent irrigation feature vectors through the feature extraction and processing module, and finally calculate the precise irrigation demand IrrigAmt. During the irrigation process, the system not only takes into account the real-time environmental data, but also makes dynamic adjustments based on the historical mean to calculate the optimal comprehensive irrigation coefficient IrrigFinal to ensure that each irrigation can achieve the expected effect. This intelligent irrigation algorithm based on the combination of real-time and historical data greatly improves the utilization rate of water resources, reduces the waste of resources caused by over-irrigation or under-irrigation, and ultimately achieves the goal of precision agriculture.

[0052] (2) The intelligent irrigation algorithm module of the system can not only calculate the basic irrigation demand, but also adapt to environmental changes in real time through dynamic adjustment units, and incorporate the adjusted demand IrrigAdj into the final irrigation decision. This flexible adaptive mechanism enables the system to quickly adjust the irrigation strategy when faced with sudden weather changes or fluctuations in soil conditions to ensure a stable supply of water for crop growth. At the same time, the result summary and execution module analyzes the effect of each irrigation, calculates the effective irrigation coefficient EffIrrig, and compares and evaluates it with the preset irrigation effect threshold G to ensure the optimization of the next irrigation strategy. This closed-loop feedback mechanism greatly enhances the adaptability and resilience of the system, and effectively avoids irrigation errors caused by changes in the external environment in traditional irrigation systems.

[0053] (3) The system is developed using the modern front-end frameworks React and Vue.js through the user interface and operation module, supporting dynamic data updates and interactive operations. Users can view the dynamic change curves of the comprehensive irrigation coefficient IrrigFinal and the effective irrigation coefficient EffIrrig in real time through an intuitive interface. The interactive elements provided by the system, such as sliders, input boxes, and knobs, allow users to manually adjust the soil irrigation weight parameter k1, meteorological weight parameter k2, and crop weight parameter k3, as well as the adjustment coefficient , and After each adjustment, the system will immediately update the results and display them in real time on the interface. This instant feedback operation mode not only enables users to flexibly adjust irrigation strategies according to actual needs, but also finds the best balance between the automatic optimization suggestions given by the system and user experience, significantly improving decision-making efficiency and reducing the complexity of manual intervention. Through this optimized design, the user experience of the system has been comprehensively improved, providing farmers with more convenient and efficient management tools. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a schematic diagram of the process of an agricultural intelligence management system based on the Internet of Things of the present invention;

[0055] Figure 2 This is a schematic diagram of the physical structure of an agricultural intelligence management system based on the Internet of Things of the present invention;

[0056] Figure 3 To invent an algorithm flow chart of an agricultural smart management system based on the Internet of Things;

[0057] Figure 4 To invent an evaluation flowchart of an agricultural smart management system based on the Internet of Things. DETAILED DESCRIPTION

[0058] 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.

[0059] Example 1

[0060] See also Figure 1 , the present invention provides an agricultural intelligent management system based on the Internet of Things. To achieve the above purpose, the present invention is implemented through the following technical solutions: including an irrigation data acquisition module, a feature extraction and processing module, an intelligent irrigation algorithm module, a result summary and execution module and a user interface and operation module;

[0061] The irrigation data collection module collects environmental data related to irrigation by installing monitoring equipment and IoT integration equipment, and summarizes and generates environmental data groups;

[0062] The feature extraction module is used to extract features from the collected environmental data group and generate intelligent irrigation feature vectors for algorithm optimization;

[0063] The intelligent irrigation algorithm module is used to calculate the irrigation demand IrrigAmt according to the generated irrigation characteristic vector, and calculate the adjustment demand IrrigAdj according to the irrigation demand IrrigAmt, and then comprehensively calculate the obtained irrigation demand IrrigAmt and the adjustment demand IrrigAdj to obtain the comprehensive irrigation coefficient IrrigFinal;

[0064] The summary and execution module is used to calculate the effective irrigation coefficient EffIrrig based on the irrigation feature vector, and preset the irrigation effect threshold G, to conduct comparative evaluation and determine the next irrigation strategy;

[0065] The user interface and operation module is used to provide users with visual irrigation decisions and results, and provide manual adjustment options.

[0066] In this embodiment, the system can accurately collect and summarize environmental data related to irrigation through the irrigation data acquisition module, and generate a comprehensive environmental data set. Subsequently, the feature extraction and processing module performs in-depth analysis on these data, extracts key features, generates intelligent irrigation feature vectors, and provides basic support for algorithm optimization. Finally, the intelligent irrigation algorithm module determines the optimal irrigation strategy through comprehensive calculation, and adjusts the irrigation decision in real time to achieve the best growth conditions for crops. Compared with traditional irrigation technology, the system realizes accurate evaluation of each irrigation effect through the result summary and execution module. The system dynamically adjusts the next irrigation strategy by comparing the effective irrigation coefficient EffIrrig with the preset irrigation effect threshold G to ensure the accuracy and reliability of irrigation. This closed-loop feedback mechanism overcomes the problem of inaccurate irrigation caused by environmental changes and insufficient data processing in traditional methods, significantly improves the utilization rate of water resources, and reduces the waste of water resources in the irrigation process. In addition, the user interface and operation module provide users with flexible adjustment options, allowing users to make refined adjustments based on real-time data, further improving the adaptability and decision-making efficiency of the system.

[0067] Example 2

[0068] This embodiment is explained in Example 1, please refer to Figure 1 Specifically: the irrigation data collection module collects soil data of the irrigation land in real time by installing detection equipment around the agricultural irrigation land, and then builds an API application interface to integrate with the IoT meteorological platform to collect meteorological data of the irrigation land in real time;

[0069] The detection equipment includes a humidity sensor and a temperature sensor;

[0070] Soil data include soil moisture SD and soil temperature WD;

[0071] Meteorological data include humidity Qsd, temperature Qwd, precipitation Qjs and wind speed Qfs;

[0072] At the same time, the planting density Zmd and crop water requirement Zxs are set based on agricultural crops;

[0073] Then the acquired soil data, meteorological data and crop irrigation factor CRP are summarized into an environmental data group;

[0074] The data from the sensors is then collected through the IoT gateway and transmitted to the feature extraction and processing module.

[0075] In this embodiment, the system realizes real-time monitoring of soil and meteorological conditions of agricultural irrigation land through efficient integration of irrigation data acquisition modules. By installing humidity and temperature sensors around the irrigation land, the system can continuously collect key data such as soil humidity SD, soil temperature WD, meteorological humidity Qsd, meteorological temperature Qwd, precipitation Qjs, and wind speed Qfs, and combine these data with the set planting density Zmd and crop water requirement Zxs to generate a comprehensive environmental data set. This data integration and aggregation process realizes efficient data collection by building a seamless connection between the API application interface and the IoT meteorological platform system.

[0076] Example 3

[0077] This embodiment is explained in Example 2. Please refer to Figure 1 ,Specifically: the feature extraction and processing module includes a ,feature extraction unit and a preprocessing unit;

[0078] The feature extraction unit is used to extract soil irrigation parameters SMT, meteorological parameters WEA and crop parameters CRP based on soil data and meteorological data in the environmental data group;

[0079] The soil irrigation parameter SMT is calculated and extracted through the soil moisture SD and soil temperature WD in the soil data. The calculation formula is: ;

[0080] The meteorological parameter WEA is calculated and extracted through the humidity Qsd, temperature Qwd, precipitation Qjs and wind speed Qfs in the meteorological data. The calculation formula is: ;

[0081] The crop parameter CRP is calculated and extracted through the set planting density Zmd and crop water requirement Zxs. The calculation formula is: ;

[0082] The preprocessing unit is used to standardize the extracted intelligent irrigation feature vectors to eliminate the influence of different dimensions, and then combine the soil irrigation parameters SMT, meteorological parameters WEA and crop parameters CRP to generate intelligent irrigation feature vectors.

[0083] In this embodiment, the feature extraction and processing module realizes in-depth analysis and optimization processing of the environmental data group through its feature extraction unit and preprocessing unit. The feature extraction unit accurately extracts soil irrigation parameters SMT, meteorological parameters WEA and crop parameters CRP based on soil data and meteorological data, thereby providing key input data for the intelligent irrigation algorithm. Through standardization, the preprocessing unit effectively eliminates the dimensional differences between different parameters, ensuring the consistency and accuracy of the data in the algorithm calculation. Finally, the system combines these key parameters to generate an intelligent irrigation feature vector, providing a reliable basis for subsequent irrigation decisions. Compared with traditional technical means, this module not only improves the efficiency of data processing, but also improves the accuracy and consistency of irrigation parameters. Traditional methods often rely on a single or simply processed data source, while this module ensures the comprehensiveness and scientificity of irrigation decisions through the comprehensive extraction and processing of multidimensional data. By optimizing the feature extraction and processing process, the system can better capture the impact of environmental changes, thereby significantly improving the accuracy and response speed of irrigation strategies, and ultimately promoting the healthy growth of crops and the improvement of agricultural production efficiency.

[0084] Example 4

[0085] This embodiment is explained in Example 3, please refer to Figure 1 and Figure 3 ,Specifically: the intelligent irrigation algorithm module includes an irrigation demand analysis unit, a ,dynamic adjustment unit and a comprehensive control unit;

[0086] The irrigation demand analysis unit is used to calculate the irrigation demand IrrigAmt of agricultural crops based on the obtained irrigation characteristic vector;

[0087] The irrigation demand IrrigAmt is calculated by the following algorithm formula;

[0088] ;

[0089] Where k1, k2 and k3 are the preset weight values ​​of soil irrigation parameter SMT, meteorological parameter WEA and crop parameter CRP respectively.

[0090] The adjustment unit is used to dynamically adapt to environmental changes by comparing the current conditions with the historical averages based on the obtained irrigation demand IrrigAmt, and to calculate and obtain the adjusted demand IrrigAdj;

[0091] The adjusted demand IrrigAdj is calculated by the following algorithm formula;

[0092] ;

[0093] Where SMTavg represents the historical average soil irrigation parameter, WEAavg represents the historical average meteorological parameter, △SMT represents the difference between the current soil irrigation parameter SMT and the historical average soil irrigation parameter SMT, and △WEA represents the difference between the current meteorological parameter WEA and the historical average meteorological parameter WEA.

[0094] The comprehensive control unit is used to integrate the irrigation demand IrrigAmt and the adjustment demand IrrigAdj, and calculate and obtain the comprehensive irrigation coefficient IrrigFinal;

[0095] The comprehensive irrigation coefficient IrrigFinal is calculated by the following algorithm formula;

[0096] ;

[0097] Where PrevIrrig represents the data of the last irrigation. , and They respectively represent the irrigation demand IrrigAmt, the adjustment demand IrrigAdj and the adjustment coefficient of the previous irrigation data PrevIrrig.

[0098] In this embodiment, the intelligent irrigation algorithm module of the system realizes the accurate calculation and dynamic adjustment of the irrigation demand of agricultural crops through the organic combination of the irrigation demand analysis unit, the dynamic adjustment unit and the comprehensive control unit. The irrigation demand analysis unit uses the extracted irrigation feature vector, combined with the soil irrigation parameter SMT, the meteorological parameter WEA and the crop parameter CRP, to calculate the precise irrigation demand IrrigAmt, providing a scientific irrigation basis for crops. The dynamic adjustment unit flexibly adjusts the irrigation strategy by introducing the comparison between the current environmental conditions and the historical mean, and calculates the adjustment demand IrrigAdj to adapt to real-time environmental changes. Finally, the comprehensive control unit integrates the irrigation demand and the adjustment demand, and calculates the comprehensive irrigation coefficient IrrigFinal in combination with the last irrigation data to ensure the comprehensiveness and effectiveness of each irrigation decision.

[0099] Example 5

[0100] This embodiment is explained in Example 4. Please refer to Figure 1 , Figure 3 and Figure 4 ,Specifically: the result summary and execution module includes an irrigation effect ,analysis unit and an execution unit;

[0101] The irrigation effect analysis unit is used to analyze the irrigation effect of each time and calculate the effective irrigation coefficient EffIrrig;

[0102] ;

[0103] Where n represents the total number of data points, Abs represents the absolute value function, IrrigFinal,i represents the actual comprehensive irrigation coefficient of the i-th data point, IrrigFinal2,i represents the target comprehensive irrigation coefficient of the i-th data point, and Wi represents the weight value of the i-th data point;

[0104] The execution unit is used to preset the irrigation effect threshold G based on the crop irrigation index, and then compare and evaluate the irrigation strategy to be executed next time with the effective irrigation coefficient EffIrrig. The specific evaluation content is as follows;

[0105] When the effective irrigation coefficient EffIrrig is less than the irrigation effect threshold G, it means that the irrigation effect is normal and the current strategy continues to be implemented;

[0106] When the effective irrigation coefficient EffIrrig ≥ irrigation effect threshold G, it means that the irrigation effect is abnormal, and the weight values ​​k1, k2 and k3 and the adjustment coefficient , and An increase of 10% is made and a reminder is generated to the staff to adjust the irrigation strategy.

[0107] In this embodiment, the result summary and execution module of the system realizes accurate evaluation and strategy optimization of each irrigation effect through the coordinated operation of the irrigation effect analysis unit and the execution unit. The irrigation effect analysis unit calculates the effective irrigation coefficient EffIrrig and compares the actual comprehensive irrigation coefficient with the target comprehensive irrigation coefficient to quantify the effectiveness of each irrigation. The execution unit compares and analyzes the preset irrigation effect threshold G with the calculated effective irrigation coefficient to determine whether the next irrigation strategy needs to be adjusted. When the irrigation effect is normal, the system maintains the current strategy; if the irrigation effect is abnormal, the system automatically adjusts the weights and adjustment coefficients of the relevant parameters to ensure that the next irrigation can better meet the needs of crops.

[0108] Example 6

[0109] This embodiment is explained in Example 5. Please refer to Figure 1 and Figure 3 Specifically: The user interface and operation module is used to develop the user interface using the modern front-end framework React and Vue.js, support dynamic data updates and interactive operations, and display the dynamic change curves of the comprehensive irrigation coefficient IrrigFinal and the effective irrigation coefficient EffIrrig in the same user interface. Sliders, input boxes and knobs are provided on the user interface to allow users to manually adjust the weights k1, k2 and k3 and the adjustment coefficients. , and After each adjustment, the system will immediately update the calculation results of the dynamic change curve of the comprehensive irrigation coefficient IrrigFinal and the effective irrigation coefficient EffIrrig, and display the change results in real time on the interface.

[0110] In this embodiment, the user interface and operation module of the system realizes the dynamic update and real-time operation of irrigation system data by developing an intuitive and highly interactive user interface using modern front-end frameworks React and Vue.js. The module can display the dynamic change curves of the comprehensive irrigation coefficient IrrigFinal and the effective irrigation coefficient EffIrrig, and provides interactive elements such as sliders, input boxes and knobs, allowing users to manually adjust the weight values ​​k1, k2, k3 and the adjustment coefficient , , After each adjustment, the system will immediately update the calculation results and display the changes in real time on the interface. This instant feedback mechanism not only enables users to flexibly adjust irrigation strategies under different environmental conditions, but also greatly improves the convenience and efficiency of operation.

[0111] Example 7

[0112] See also Figure 1 and Figure 2 A computer device comprises: a memory and a processor; the memory stores a computer program, and when the processor executes the computer program, an agricultural smart management system based on the Internet of Things is implemented to monitor the actual effect of the irrigation process and provide instant feedback.

[0113] Example 8

[0114] See also Figure 1 and Figure 2 , a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an agricultural smart management system based on the Internet of Things.

[0115] 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. An agricultural intelligent management system based on the Internet of Things, characterized by: It includes irrigation data acquisition module, feature extraction and processing module, intelligent irrigation algorithm module, result summary and execution module and user interface and operation module; The irrigation data collection module collects environmental data related to irrigation by installing monitoring equipment and Internet of Things integrated equipment, and summarizes and generates an environmental data group; The feature extraction and processing module is used to extract features from the collected environmental data group and generate an irrigation feature vector for algorithm optimization; The intelligent irrigation algorithm module is used to calculate and obtain the irrigation demand IrrigAmt according to the generated irrigation characteristic vector, and calculate and obtain the adjustment demand IrrigAdj according to the irrigation demand IrrigAmt, and then comprehensively calculate the obtained irrigation demand IrrigAmt and the adjustment demand IrrigAdj to obtain the comprehensive irrigation coefficient IrrigFinal; The result summary and execution module is used to calculate the effective irrigation coefficient EffIrrig based on the comprehensive irrigation coefficient IrrigFinal, and preset the irrigation effect threshold G for secondary comparative evaluation to determine the next irrigation strategy; The user interface and operation module are used to provide users with visualized irrigation decisions and results, and provide manual adjustment options; The irrigation data collection module collects soil data of the irrigation land in real time by installing monitoring equipment around the agricultural irrigation land, and then constructs an API application program interface to integrate with the Internet of Things meteorological platform to collect meteorological data of the irrigation land in real time; The monitoring device includes a humidity sensor and a temperature sensor; The soil data include soil moisture SD and soil temperature WD; The meteorological data include humidity Qsd, temperature Qwd, precipitation Qjs and wind speed Qfs; At the same time, the planting density Zmd and crop water requirement Zxs are set based on the crops; Then the acquired soil data, meteorological data, planting density Zmd and crop water requirement Zxs are summarized to obtain the environmental data group; Collect and transmit the environmental data set to the feature extraction and processing module through the IoT gateway; The feature extraction and processing module includes a feature extraction unit and a preprocessing unit; The feature extraction unit is used to extract features based on soil data, meteorological data, planting density Zmd and crop water requirement Zxs in the environmental data group to obtain soil irrigation parameters SMT, meteorological parameters WEA and crop parameters CRP; The soil irrigation parameter SMT is obtained by calculating and extracting the soil moisture SD and soil temperature WD in the soil data. The calculation formula is: ; The meteorological parameter WEA is obtained by calculating and extracting the humidity Qsd, temperature Qwd, precipitation Qjs and wind speed Qfs in the meteorological data. The calculation formula is: ; The crop parameter CRP is obtained by calculating and extracting the set planting density Zmd and crop water requirement Zxs. The calculation formula is: ; The preprocessing unit is used to standardize the extracted irrigation feature vector, eliminate the influence of different dimensions, and then combine the soil irrigation parameter SMT, the meteorological parameter WEA and the crop parameter CRP to generate the irrigation feature vector; The intelligent irrigation algorithm module includes an irrigation demand analysis unit, a dynamic adjustment unit and a comprehensive control unit; The irrigation demand analysis unit is used to calculate the irrigation demand IrrigAmt of the crop according to the obtained irrigation characteristic vector; The irrigation demand IrrigAmt is calculated by the following algorithm formula: ； Where k1, k2 and k3 are the preset weight values ​​of soil irrigation parameter SMT, meteorological parameter WEA and crop parameter CRP respectively; The dynamic adjustment unit is used to calculate and obtain the adjustment demand IrrigAdj by comparing the current conditions with the historical average according to the obtained irrigation demand IrrigAmt, so as to dynamically adapt to the changes in the environment; The adjustment demand IrrigAdj is calculated by the following algorithm formula: ； In the formula, SMTavg represents the historical average soil irrigation parameter, WEAavg represents the historical average meteorological parameter, △SMT represents the difference between the current soil irrigation parameter SMT and the historical average soil irrigation parameter SMTavg, and △WEA represents the difference between the current meteorological parameter WEA and the historical average meteorological parameter WEAavg; The comprehensive control unit is used to integrate the irrigation demand IrrigAmt and the adjustment demand IrrigAdj to calculate and obtain the comprehensive irrigation coefficient IrrigFinal; The comprehensive irrigation coefficient IrrigFinal is calculated by the following algorithm formula: ； Where PrevIrrig represents the data of the last irrigation. , and They represent the irrigation demand IrrigAmt, the adjustment demand IrrigAdj and the adjustment coefficient of the previous irrigation data PrevIrrig respectively; The result summary and execution module includes an irrigation effect analysis unit and an execution unit; The irrigation effect analysis unit is used to analyze the irrigation effect of each time and calculate and obtain the effective irrigation coefficient EffIrrig; ； Where n represents the total number of data points, Abs represents the absolute value function, IrrigFinal,i represents the actual comprehensive irrigation coefficient of the i-th data point, IrrigFinal2,i represents the target comprehensive irrigation coefficient of the i-th data point, and Wi represents the weight value of the i-th data point; The execution unit is used to preset the irrigation effect threshold G based on the crop irrigation index, and then compare it with the effective irrigation coefficient EffIrrig to evaluate and analyze the irrigation strategy to be executed next time. The specific evaluation content is as follows: When the effective irrigation coefficient EffIrrig is less than the irrigation effect threshold G, it means that the irrigation effect is normal and the current irrigation strategy continues to be implemented; When the effective irrigation coefficient EffIrrig ≥ irrigation effect threshold G, it means that the irrigation effect is abnormal, and the weight values ​​k1, k2 and k3 and the adjustment coefficient , and Increase by 10% and prompt staff to adjust irrigation strategies.

2. According to claim 1, an agricultural intelligence management system based on the Internet of Things is characterized by: The user interface and operation module is used to develop user interfaces using the front-end frameworks React and Vue.js, supports dynamic data updates and interactive operations, and uses the same specification user interface to display the dynamic change curves of the comprehensive irrigation coefficient IrrigFinal and the effective irrigation coefficient EffIrrig. Sliders, input boxes and knob interactive elements are provided on the user interface to allow users to manually adjust the weight values ​​k1, k2 and k3 and the adjustment coefficients. , and After each adjustment, the system will immediately update the calculation results of the dynamic change curve of the comprehensive irrigation coefficient IrrigFinal and the effective irrigation coefficient EffIrrig, and display the calculation results in real time on the user interface.

3. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that when the processor executes the computer program, an agricultural smart management system based on the Internet of Things as described in any one of claims 1 to 2 is implemented.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, an agricultural smart management system based on the Internet of Things as described in any one of claims 1 to 2 is implemented.

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