Crop nutrient absorption condition intelligent analysis method and system

Abstract

The invention discloses a crop nutrient absorption condition intelligent analysis method and system. The method comprises the steps that server data content is divided into a public area and a limited area; obtaining all access terminals accessing the server, dividing the access terminals into a public area and a limited area based on the access history of the access terminals, and accessing all newly accessed access terminals into the public area; acquiring an access behavior of a newly accessed access terminal in the public area in real time, matching the access behavior with a historical abnormal behavior in the database, and performing abnormality judgment based on a matching result; and dividing the newly accessed access terminal into different security levels based on an abnormal judgment result, and performing different security measures. The method has the advantages that the abnormal behaviors are matched by analyzing the abnormal behavior characteristics, and security threats are effectively prevented. And through differentiated management, real-time monitoring and flow limitation, the stability and safety of the server are guaranteed.

Description

Technical Field

[0001] The invention relates to nutrient absorption monitoring, and in particular to an intelligent analysis method and system for nutrient absorption status of crops. Background Art

[0002] An intelligent analysis method and system for the nutrient absorption status of crops, based on the historical nutrient solution absorption data of various crops, analyzes the nutrient solution consumption characteristics of different growth cycles under normal growth conditions, and constructs a corresponding nutrient absorption model. By real-time monitoring of the changes in the nutrient solution of the current crops during their growth, real-time data is obtained and compared with the preset nutrient absorption model to identify whether there are any abnormalities. If the nutrient solution consumption of the crops is monitored to be inconsistent with the normal pattern, the system can promptly determine the growth abnormality and adjust the nutrient solution delivery strategy to ensure that the crops receive appropriate nutrient support. This method helps to accurately manage nutrients during the growth of crops, and can also continuously optimize the nutrient absorption model through a comprehensive evaluation of the crops after the growth cycle.

[0003] The current methods for analyzing the nutrient absorption status of crops on the market mainly include soil nutrient testing, leaf analysis, plant growth monitoring and remote sensing technology. Soil nutrient testing provides basic data for the nutrient needs of crops by chemically analyzing the content of major nutrients in the soil; leaf analysis reflects the nutritional status of crops by detecting the nutrient levels in crop leaves; plant growth monitoring indirectly evaluates nutrient absorption by observing the appearance characteristics of crops, such as leaf color and plant height; remote sensing technology uses drones or satellite remote sensing to analyze spectral data to monitor the nutritional status of large areas of farmland in real time. The combined application of these methods will help achieve precise fertilization and scientific management, and improve crop yield and quality. Summary of the invention

[0004] In order to improve the existing crop nutrient absorption status analysis method and system, an intelligent crop nutrient absorption status analysis method and system is provided. This method monitors the changes in crop nutrient solution in real time and compares it with historical data to determine whether its growth is abnormal. According to the analysis results, the system automatically adjusts the nutrient solution delivery strategy and continuously optimizes the nutrient absorption model to improve crop growth efficiency and quality.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A method for intelligent analysis of crop nutrient absorption status, characterized by comprising:

[0007] Based on the historical nutrient solution absorption data of each crop, the nutrient solution consumption characteristics in each growth cycle under normal conditions are obtained, and a nutrient absorption model is constructed;

[0008] Obtain the current crop growth cycle, monitor the changes in the nutrient solution in its nutrient bag in real time, and obtain the nutrient solution absorption data in each growth cycle;

[0009] Compare the real-time monitored changes in nutrient solution with the nutrient absorption model data to determine whether the current crop growth is abnormal;

[0010] Adjust the nutrient solution infusion strategy based on abnormal crop growth;

[0011] Based on the growth of crops after the end of each crop growth cycle, a comprehensive assessment is conducted to improve the nutrient absorption model.

[0012] Preferably, the acquisition of nutrient solution consumption characteristics in each growth cycle under normal conditions based on historical nutrient solution absorption data of each crop and the construction of a nutrient absorption model specifically include:

[0013] Based on the different stages of the crop growth cycle, it is divided into seedling stage, growth stage, flowering stage, and maturity stage;

[0014] Based on the absorption of nutrient solution in each stage divided in the historical data, it is divided into different time series ;

[0015] Based on the time series in each stage, the daily nutrient solution consumption in each stage is calculated:

[0016]

[0017] in, is the consumption of nutrient solution on day i, is the consumption of nutrient solution on day i-1;

[0018] Based on the daily nutrient solution consumption, calculate the average nutrient solution consumption rate:

[0019]

[0020] in, is the number of days in each stage, is the nutrient solution consumption on day i, , The start and end time of this phase;

[0021] Based on the regular changes in nutrient consumption of various crops in different time periods or seasons, the time series is analyzed by Fourier transform, and the formula is:

[0022]

[0023] in, is a constant term, indicating the basic consumption, , is the Fourier transform coefficient, which represents the periodic characteristics of different frequencies, T is the period length, and t is the time point;

[0024] Based on the daily nutrient solution consumption formula, the average nutrient solution consumption rate formula and the periodic change law formula, a comprehensive nutrient absorption model is obtained.

[0025] Preferably, the obtaining of the current crop growth cycle, real-time monitoring of changes in the nutrient solution in the nutrient bag, and obtaining the nutrient solution absorption data in each growth cycle specifically include:

[0026] Based on sensor equipment, record the real-time consumption of nutrient solution;

[0027] Based on the real-time consumption of nutrient solution, obtain the daily nutrient solution consumption data, daily nutrient solution absorption rate data, and nutrient solution demand change pattern data during each growth cycle.

[0028] Preferably, comparing the real-time monitored nutrient solution changes with the nutrient absorption model data to determine whether the current crop growth is abnormal specifically includes:

[0029] Based on the acquired time series A(t), it is divided into three parts, including trend component, seasonal component and residual component. The formula is:

[0030]

[0031] in, is the trend component, which represents the long-term changes in the data. is the seasonal component, which represents the periodic fluctuation part in the data. The residual component represents the remaining part after removing the trend and seasonality, which is usually composed of noise and abnormal changes;

[0032] Based on the comparison of residual components and nutrient absorption model data, it is determined whether there is growth abnormality of crops.

[0033] Preferably, judging whether crops have abnormal growth based on comparing residual components with nutrient absorption model data specifically includes:

[0034] The difference between the actual nutrient solution consumed and the absorption rate expected by the model is obtained as follows:

[0035]

[0036] Get the cumulative difference over a period of time. The formula is:

[0037]

[0038] The residual extracted by seasonal decomposition It is a random fluctuation. If the cumulative difference over a period of time exceeds the preset normal range, it is judged that the growth of crops is abnormal.

[0039] Preferably, the adjusting of the nutrient solution infusion strategy based on the abnormal growth of crops specifically includes:

[0040] Set the target nutrient solution remaining amount based on the nutrient solution remaining amount , the adjustment formula is:

[0041]

[0042] in, is the current remaining amount of nutrient solution, is the adjustment coefficient, used to adjust the range of change;

[0043] Based on the growth stage of each crop, the corresponding nutrient ratio is set and the formula is adjusted regularly. For example, in the seedling stage of crops, more nitrogen is added to promote root development. In the flowering and fruiting period, more phosphorus, potassium and other elements are added to promote reproductive growth.

[0044] Preferably, the comprehensive evaluation based on the growth of crops after the growth cycle of each crop is completed to improve the nutrient absorption model specifically includes:

[0045] The accuracy of the evaluation model is obtained by calculating the mean square error between the actual nutrient consumption growth and the data in the nutrient absorption model. The mean square error formula is:

[0046]

[0047] Where n is the number of time nodes in the time series, is the actual value and the model data value;

[0048] Based on the size of the mean square error value obtained by calculation, adjust the parameters of each formula in the model to reduce the size of the mean square error.

[0049] Furthermore, a crop nutrient absorption status intelligent analysis system is characterized by comprising:

[0050] Nutrient absorption model building module: The nutrient absorption model building module is mainly used to obtain the nutrient solution consumption characteristics in each growth cycle under normal circumstances based on the historical nutrient solution absorption data of each crop, and to build a nutrient absorption model;

[0051] Nutrient solution data monitoring and collection module: The nutrient solution data monitoring and collection module is mainly used to obtain the current crop growth cycle, monitor the changes in the nutrient solution in its nutrient bag in real time, and obtain the nutrient solution absorption data in each growth cycle;

[0052] Data comparison module: The data comparison module is mainly used to compare the real-time monitored changes in the nutrient solution with the nutrient absorption model data;

[0053] Abnormality detection module: The abnormality detection module is mainly used to determine whether the current crop growth situation is abnormal based on the comparison situation;

[0054] Strategy adjustment module: The strategy adjustment module is mainly used to adjust the nutrient solution infusion strategy based on abnormal crop growth;

[0055] Feedback improvement module: The feedback improvement module is mainly used to conduct a comprehensive assessment and improve the nutrient absorption model based on the growth conditions of crops after the growth cycle of each crop;

[0056] Processor: The processor is mainly used for calculation of various formulas and error calculation in the nutrient absorption model.

[0057] Compared with the prior art, the advantages of the present invention are:

[0058] Constructing a nutrient absorption model based on historical nutrient solution absorption data can accurately reflect the nutritional needs and consumption characteristics of various crops in different growth cycles, thereby providing a scientific basis for the refined management of crops. Real-time monitoring of changes in nutrient solution during the current crop growth cycle can promptly detect abnormal growth and avoid growth problems caused by unbalanced or insufficient nutrient supply. By comparing with the nutrient absorption model data, the system can automatically determine whether there are abnormalities and dynamically adjust the nutrient solution delivery strategy based on the results, thereby optimizing the crop growth environment and improving crop yield and quality. Based on a comprehensive evaluation after the end of the crop growth cycle, the system can continuously improve the nutrient absorption model, improve the accuracy and applicability of the prediction, and form a virtuous circle. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A schematic diagram of the analysis method of the method and system proposed in the present invention;

[0060] Figure 2 A schematic diagram of constructing a nutrient absorption model for the method and system proposed in the present invention;

[0061] Figure 3 A schematic diagram of real-time data acquisition of the method and system proposed in the present invention;

[0062] Figure 4 A schematic diagram of abnormal growth judgment of the method and system proposed by the present invention;

[0063] Figure 5 A schematic diagram of comparison and judgment of the method and system proposed by the present invention;

[0064] Figure 6 A schematic diagram of strategy adjustment of the method and system proposed in the present invention;

[0065] Figure 7 A schematic diagram of the model improvement of the method and system proposed in the present invention;

[0066] Figure 8 This is a schematic diagram of the electronic device in this solution;

[0067] Fig. 9 This is a schematic diagram of the computer-readable storage medium structure in this solution. DETAILED DESCRIPTION

[0068] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0069] An intelligent analysis system for crop nutrient absorption status, comprising:

[0070] Nutrient absorption model building module: The nutrient absorption model building module is mainly used to obtain the nutrient solution consumption characteristics in each growth cycle under normal circumstances based on the historical nutrient solution absorption data of each crop, and to build a nutrient absorption model;

[0071] Nutrient solution data monitoring and collection module: The nutrient solution data monitoring and collection module is mainly used to obtain the current crop growth cycle, monitor the changes in the nutrient solution in its nutrient bag in real time, and obtain the nutrient solution absorption data in each growth cycle;

[0072] Data comparison module: The data comparison module is mainly used to compare the real-time monitored changes in the nutrient solution with the nutrient absorption model data;

[0073] Abnormality detection module: The abnormality detection module is mainly used to determine whether the current crop growth situation is abnormal based on the comparison situation;

[0074] Strategy adjustment module: The strategy adjustment module is mainly used to adjust the nutrient solution infusion strategy based on abnormal crop growth;

[0075] Feedback improvement module: The feedback improvement module is mainly used to conduct a comprehensive assessment and improve the nutrient absorption model based on the growth conditions of crops after the growth cycle of each crop;

[0076] Processor: The processor is mainly used for calculation of various formulas and error calculation in the nutrient absorption model.

[0077] See also Figure 1 As shown, a method for intelligent analysis of nutrient absorption status of crops comprises:

[0078] Step 1: Based on the historical nutrient solution absorption data of each crop, obtain the nutrient solution consumption characteristics in each growth cycle under normal conditions and build a nutrient absorption model;

[0079] Step 2: Obtain the current crop growth cycle, monitor the changes in the nutrient solution in its nutrient bag in real time, and obtain the nutrient solution absorption data in each growth cycle;

[0080] Step 3: Compare the real-time monitored changes in the nutrient solution with the nutrient absorption model data to determine whether the current crop growth is abnormal;

[0081] Step 4: Compare the real-time monitored changes in the nutrient solution with the nutrient absorption model data to determine whether the current crop growth is abnormal;

[0082] Step 5: Conduct a comprehensive assessment based on the growth of crops after the end of each crop growth cycle and improve the nutrient absorption model.

[0083] See also Figure 2 As shown in the figure, based on the historical nutrient solution absorption data of each crop, the nutrient solution consumption characteristics in each growth cycle under normal circumstances are obtained, and the nutrient absorption model is constructed, which specifically includes:

[0084] Based on the different stages of the crop growth cycle, it is divided into seedling stage, growth stage, flowering stage, and maturity stage;

[0085] Based on the absorption of nutrient solution in each stage divided in the historical data, it is divided into different time series ;

[0086] Based on the time series in each stage, the daily nutrient solution consumption in each stage is calculated:

[0087]

[0088] in, is the consumption of nutrient solution on day i, is the consumption of nutrient solution on day i-1;

[0089] Based on the daily nutrient solution consumption, calculate the average nutrient solution consumption rate:

[0090]

[0091] in, is the number of days in each stage, is the nutrient solution consumption on day i, , The start and end time of this phase;

[0092] Based on the regular changes in nutrient consumption of various crops in different time periods or seasons, the time series is analyzed by Fourier transform, and the formula is:

[0093]

[0094] in, is a constant term, indicating the basic consumption, , is the Fourier transform coefficient, which represents the periodic characteristics of different frequencies, T is the period length, and t is the time point;

[0095] Based on the daily nutrient solution consumption formula, the average nutrient solution consumption rate formula and the periodic change law formula, a comprehensive nutrient absorption model is obtained.

[0096] Specifically, the nutrient solution consumption data for certain growth cycles or crop varieties may be less or unbalanced, which may lead to lower prediction accuracy of the model for these stages or varieties. Therefore, for stages with less data, data augmentation methods can be considered, such as synthesizing data through simulated data generators, or weighting stages or varieties with less data so that the model pays more attention to these stages, and cross-validation techniques can be used to reduce overfitting and ensure the generalization ability of the model with less data.

[0097] See also Figure 3 As shown, the current crop growth cycle is obtained, the changes in the nutrient solution in the nutrient bag are monitored in real time, and the nutrient solution absorption data in each growth cycle are obtained, including:

[0098] Based on sensor equipment, record the real-time consumption of nutrient solution;

[0099] Based on the real-time consumption of nutrient solution, obtain the daily nutrient solution consumption data, daily nutrient solution absorption rate data, and nutrient solution demand change pattern data during each growth cycle.

[0100] See also Figure 4 As shown, the real-time monitored changes in nutrient solution are compared with the nutrient absorption model data to determine whether the current crop growth is abnormal. Specifically, the following are included:

[0101] Based on the acquired time series A(t), it is divided into three parts, including trend component, seasonal component and residual component. The formula is:

[0102]

[0103] in, is the trend component, which represents the long-term changes in the data. is the seasonal component, which represents the periodic fluctuation part in the data. The residual component represents the remaining part after removing the trend and seasonality, which is usually composed of noise and abnormal changes;

[0104] Based on the comparison of residual components and nutrient absorption model data, it is determined whether there is growth abnormality of crops.

[0105] See also Figure 5 As shown in the figure, based on the comparison of residual components and nutrient absorption model data, judging whether there is abnormal growth of crops specifically includes:

[0106] The difference between the actual nutrient solution consumed and the absorption rate expected by the model is obtained as follows:

[0107]

[0108] Get the cumulative difference over a period of time. The formula is:

[0109]

[0110] The residual extracted by seasonal decomposition It is a random fluctuation. If the cumulative difference over a period of time exceeds the preset normal range, it is judged that the growth of crops is abnormal.

[0111] It is understandable that if the judgment criteria are too strict, some potential anomalies may be missed; and if the judgment criteria are too loose, normal fluctuations may be misjudged as abnormalities. By comparing with historical data, an appropriate fluctuation range can be set to avoid misjudgment of extreme anomalies. At the same time, a feedback mechanism can be established to continuously monitor the prediction effect of the model. If a large number of misjudged anomalies are found, the model and judgment criteria can be adjusted in a timely manner.

[0112] See also Figure 6 Based on the abnormal growth of crops, the nutrient solution infusion strategy is adjusted, including:

[0113] Set the target nutrient solution remaining amount based on the nutrient solution remaining amount , the adjustment formula is:

[0114]

[0115] in, is the current remaining amount of nutrient solution, is the adjustment coefficient, used to adjust the range of change;

[0116] Based on the growth stage of each crop, the corresponding nutrient ratio is set and the formula is adjusted regularly. For example, in the seedling stage of crops, more nitrogen is added to promote root development. In the flowering and fruiting period, more phosphorus, potassium and other elements are added to promote reproductive growth.

[0117] See also Figure 7 As shown in the figure, a comprehensive assessment is conducted based on the growth of crops after the growth cycle of each crop, and the nutrient absorption model is improved, including:

[0118] The accuracy of the evaluation model is obtained by calculating the mean square error between the actual nutrient consumption growth and the data in the nutrient absorption model. The mean square error formula is:

[0119]

[0120] Where n is the number of time nodes in the time series, is the actual value and the model data value;

[0121] Based on the size of the mean square error value obtained by calculation, adjust the parameters of each formula in the model to reduce the size of the mean square error.

[0122] Furthermore, the method according to the embodiment of the present application can also be performed by Figure 8 The electronic device architecture shown in FIG. Figure 8 As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store a method and system for intelligent analysis of nutrient absorption status of crops provided in the present application. The electronic device 500 may also include a terminal interface 508. Of course, Figure 7 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 7 One or more components of an electronic device are shown.

[0123] Fig. 9 Schematic diagram of a computer-readable storage medium structure provided by an embodiment of the present application. Fig. 9As shown, a computer-readable storage medium 600 according to an embodiment of the present application is shown. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are executed by the processor, a method and system for intelligent analysis of nutrient absorption status of crops according to an embodiment of the present application described with reference to the above figures can be executed. The storage medium 600 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache). Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0124] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0125] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0126] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent analysis method for nutrient absorption status of crops, characterized in that: include: Based on the historical nutrient solution absorption data of each crop, the nutrient solution consumption characteristics in each growth cycle under normal conditions are obtained, and a nutrient absorption model is constructed; Obtain the current crop growth cycle, monitor the changes in the nutrient solution in its nutrient bag in real time, and obtain the nutrient solution absorption data in each growth cycle; Compare the real-time monitored changes in nutrient solution with the nutrient absorption model data to determine whether the current crop growth is abnormal; Adjust the nutrient solution infusion strategy based on abnormal crop growth; Based on the growth of crops after the end of each crop growth cycle, a comprehensive assessment is conducted to improve the nutrient absorption model.

2. The method for intelligent analysis of crop nutrient absorption status according to claim 1, characterized in that: The method of obtaining the nutrient solution consumption characteristics in each growth cycle under normal conditions based on the historical nutrient solution absorption data of each crop and constructing a nutrient absorption model specifically includes: Based on the different stages of the crop growth cycle, it is divided into seedling stage, growth stage, flowering stage, and maturity stage; Based on the absorption of nutrient solution in each stage divided in the historical data, it is divided into different time series ； Based on the time series in each stage, the daily nutrient solution consumption in each stage is calculated: in, is the consumption of nutrient solution on day i, is the consumption of nutrient solution on day i-1; Based on the daily nutrient solution consumption, calculate the average nutrient solution consumption rate: in, is the number of days in each stage, is the nutrient solution consumption on day i, , The start and end time of this phase; Based on the regular changes in nutrient consumption of various crops in different time periods or seasons, the time series is analyzed by Fourier transform, and the formula is: in, is a constant term, indicating the basic consumption, , is the Fourier transform coefficient, which represents the periodic characteristics of different frequencies, T is the period length, and t is the time point; Based on the daily nutrient solution consumption formula, the average nutrient solution consumption rate formula and the periodic change law formula, a comprehensive nutrient absorption model is obtained.

3. The method for intelligent analysis of crop nutrient absorption status according to claim 1, characterized in that: The obtaining of the current crop growth cycle, real-time monitoring of changes in the nutrient solution in the nutrient bag, and obtaining the nutrient solution absorption data in each growth cycle specifically include: Based on sensor equipment, record the real-time consumption of nutrient solution; Based on the real-time consumption of nutrient solution, obtain the daily nutrient solution consumption data, daily nutrient solution absorption rate data, and nutrient solution demand change pattern data during each growth cycle.

4. The method for intelligent analysis of crop nutrient absorption status according to claim 1, characterized in that: The real-time monitored nutrient solution changes are compared with the nutrient absorption model data to determine whether the current crop growth is abnormal, specifically including: Based on the acquired time series A(t), it is divided into three parts, including trend component, seasonal component and residual component. The formula is: in, is the trend component, which represents the long-term changes in the data. is the seasonal component, which represents the periodic fluctuation part in the data. The residual component represents the remaining part after removing the trend and seasonality, which is usually composed of noise and abnormal changes; Based on the comparison of residual components and nutrient absorption model data, it is determined whether there is growth abnormality of crops.

5. The method for intelligent analysis of nutrient absorption status of crops according to claim 4, characterized in that: The method of judging whether crops have abnormal growth based on the comparison of residual components and nutrient absorption model data specifically includes: The difference between the actual nutrient solution consumed and the absorption rate expected by the model is obtained as follows: Get the cumulative difference over a period of time. The formula is: The residual extracted by seasonal decomposition It is a random fluctuation. If the cumulative difference over a period of time exceeds the preset normal range, it is judged that the growth of crops is abnormal.

6. The method for intelligent analysis of crop nutrient absorption status according to claim 1, characterized in that: The adjustment of the nutrient solution infusion strategy based on the abnormal growth of crops specifically includes: Set the target nutrient solution remaining amount based on the nutrient solution remaining amount , the adjustment formula is: in, is the current remaining amount of nutrient solution, is the adjustment coefficient, used to adjust the range of change; Based on the growth stage of each crop, the corresponding nutrient ratio is set and the formula is adjusted regularly. For example, in the seedling stage of crops, more nitrogen is added to promote root development. In the flowering and fruiting period, more phosphorus, potassium and other elements are added to promote reproductive growth.

7. The method for intelligent analysis of crop nutrient absorption status according to claim 1, characterized in that: The above-mentioned comprehensive evaluation based on the growth of crops after the growth cycle of each crop is completed and the improvement of the nutrient absorption model specifically includes: The accuracy of the evaluation model is obtained by calculating the mean square error between the actual nutrient consumption growth and the data in the nutrient absorption model. The mean square error formula is: Where n is the number of time nodes in the time series, is the actual value and the model data value; Based on the size of the mean square error value obtained by calculation, adjust the parameters of each formula in the model to reduce the size of the mean square error.

8. Combined with a method for intelligent analysis of nutrient absorption status of crops, it is used to implement an intelligent analysis system for nutrient absorption status of crops as claimed in any one of claims 1 to 7, characterized in that: include: Nutrient absorption model building module: The nutrient absorption model building module is mainly used to obtain the nutrient solution consumption characteristics in each growth cycle under normal circumstances based on the historical nutrient solution absorption data of each crop, and to build a nutrient absorption model; Nutrient solution data monitoring and collection module: The nutrient solution data monitoring and collection module is mainly used to obtain the current crop growth cycle, monitor the changes in the nutrient solution in its nutrient bag in real time, and obtain the nutrient solution absorption data in each growth cycle; Data comparison module: The data comparison module is mainly used to compare the real-time monitored changes in the nutrient solution with the nutrient absorption model data; Abnormality detection module: The abnormality detection module is mainly used to determine whether the current crop growth situation is abnormal based on the comparison situation; Strategy adjustment module: The strategy adjustment module is mainly used to adjust the nutrient solution infusion strategy based on abnormal crop growth; Feedback improvement module: The feedback improvement module is mainly used to conduct a comprehensive assessment and improve the nutrient absorption model based on the growth conditions of crops after the growth cycle of each crop; Processor: The processor is mainly used for calculation of various formulas and error calculation in the nutrient absorption model.

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the intelligent analysis method for nutrient absorption status of crops as described in any one of claims 1-7.

10. A computer-readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by a processor, an intelligent analysis method for crop nutrient absorption status according to any one of claims 1 to 7 is implemented.

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