A garden soil quality detection system and method based on multi-source data

Abstract

The invention discloses a garden soil quality detection system and method based on multi-source data, which relates to the technical field of soil quality detection, and comprises a soil area division module, a detection database construction module, a target detection area analysis module, a correlation storage module, an abnormal growth node determination module, a response model construction module and a real-time detection module; the soil area division module is used for dividing the soil detection area based on the difference of plant species in the monitored garden; the detection database construction module is used for inputting soil quality detection events into the detection system to form a detection database; the target detection area analysis module is used for performing verification analysis on a first detection area to be analyzed to obtain a target detection area where there is a detection frequency influence optimization processing operation; the correlation storage module is used for performing data correlation storage on a second detection area to be analyzed; and the abnormal growth node determination module is used for determining abnormal growth nodes for plants in the target detection area.

Description

Technical Field

[0001] The present invention relates to the technical field of soil quality detection, and in particular to a garden soil quality detection system and method based on multi-source data. Background Art

[0002] Soil is the basis of plant growth, and its nutrient content, pH, organic matter and other factors directly affect the growth and development of plants. Soil testing can timely understand the soil conditions and provide a suitable growth environment for plants. The setting of the detection frequency can ensure the accuracy and timeliness of soil information and provide a scientific basis for plant management. In the prior art, soil monitoring has been achieved by setting up a soil moisture monitoring station to intelligently obtain soil data and environmental data to realize real-time soil status monitoring. However, when different plants are planted in the soil of the garden, there are differences in the growth status of different plants. Fixed soil testing may not be able to effectively adapt to the planted plants, so that the growth status problems of the plants cannot be discovered in a timely and effective manner, resulting in a reduction in the application efficiency of the soil moisture monitoring station. Summary of the invention

[0003] The object of the present invention is to provide a garden soil quality detection system and method based on multi-source data to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solution: a garden soil quality detection method based on multi-source data, the detection method comprising the following steps:

[0005] Step S100: Divide the monitored garden into soil detection areas based on the types of planted plants, with each soil detection area representing a unique type of plant; input the soil quality detection events of each soil detection area during the planting cycle into the detection system to form a detection database;

[0006] Step S200: Based on the detection database, the soil detection area corresponding to the soil optimization treatment operation recorded in the monitored garden is selected as the first detection area to be analyzed, and the soil detection area corresponding to the soil optimization treatment operation not recorded is the second detection area to be analyzed; the first detection area to be analyzed is verified and analyzed to obtain the target detection area where the detection frequency affects the optimization treatment operation; the second detection area to be analyzed is stored in data association;

[0007] Step S300: extracting plant remote sensing analysis data stored in the target detection area using remote sensing technology, and determining abnormal growth nodes of plants in the target detection area based on various soil quality detection events;

[0008] Step S400: obtaining soil detection data and plant remote sensing analysis data corresponding to the abnormal growth node, and constructing a detection frequency adjustment response model for plants in the target detection area;

[0009] Step S500: When there are plants of the same type monitored in real time that meet the corresponding detection frequency adjustment response model, a corresponding signal is transmitted to the detection system for frequency adjustment.

[0010] Furthermore, step S200 includes the following specific steps:

[0011] Soil optimization treatment operations refer to operations that require treatment and restoration after soil quality testing;

[0012] Extract the detection time of the soil quality detection event recorded in the first detection area to be analyzed, and calculate the interval time T between two adjacent detection events based on the detection time 1 , will record the interval time T of all soil quality detection events generated 1 If the frequencies are the same, the first detection area to be analyzed is marked as the same-frequency detection area to be analyzed, otherwise it is marked as the different-frequency detection area to be analyzed;

[0013] All soil quality detection events in the same-frequency detection area to be analyzed are recorded in chronological order to generate a same-frequency time axis sequence, and the detection data of each soil quality detection event is recorded with the detection time as the coordinate point; the coordinate point corresponding to the detection time of the soil optimization treatment operation is marked as the target coordinate point, and the life cycle of the corresponding type of plant is divided into the pre-detection life cycle and the post-detection life cycle with the target coordinate point as the segmentation point;

[0014] The first detection area to be analyzed corresponding to the same life cycle length recorded by the detection system and the ratio of the life cycle length before detection to the life cycle length after detection being greater than the similarity threshold is extracted as the control detection area;

[0015] Extract the test data recorded in the same frequency detection area to be analyzed. The test data refers to the soil data transmitted by the real-time soil moisture monitoring system. Determine the soil data for the soil optimization operation in the same frequency detection area to be analyzed as the target data. Use the formula: U 1 =[|u 1 -u 0 | / |u 2 -u 1 |] max ,

[0016] Calculate the soil index change rate U of the same frequency area to be analyzed 1 , where u 0 Indicates the target data value recorded in the soil quality detection event in the same frequency detection area to be analyzed, u 1Indicates the target data value of the first adjacent detection event record in the life cycle before the soil quality detection event, u 2 Indicates record u 1 The target data value of the first adjacent detection event record in the life cycle before the detection; [|u 1 -u 0 | / |u 2 -u 1 |] max Indicates that when there are multiple target data types, the change rate of the target data type with the largest calculation ratio is selected as the output value;

[0017] The soil index change rate calculation formula is used to calculate the test data in the control test area to obtain the corresponding soil index change rate. The formula is: Q={[∑(UU 0 ) 2 ] / N} 1 / 2 ; Calculate the soil index anomaly discrete value Q corresponding to the same frequency detection area to be analyzed, where U 0 It represents the average value of soil index change rate of the corresponding same-frequency detection area to be analyzed and the control detection area, and N represents the total number of soil index change rates;

[0018] Set the soil index abnormal discrete value threshold Q 0 , extract Q 0 The corresponding same-frequency detection area to be analyzed is the target detection area.

[0019] Q 0 This indicates that in the process of comparing with historical data, if the soil data recorded by the real-time soil moisture monitoring system for plants corresponding to similar detection areas at abnormal time nodes have similar fluctuations, the impact of data fluctuation similarity caused by unreasonable detection frequency will be higher.

[0020] Furthermore, step S200 also includes the following specific steps:

[0021] All soil quality detection events recorded in the hetero-frequency detection area to be analyzed are generated into a hetero-frequency time axis sequence in chronological order, and the target coordinate points in the hetero-frequency time axis sequence, as well as the life cycle before detection and the life cycle after detection are marked;

[0022] The interval between the detection time closest to the target coordinate point in the life cycle before the detection is constructed as the first detection period L 1 , the adjacent detection time of the first detection cycle in the life cycle before detection constitutes the second detection cycle L 2 ; Based on the first detection cycle, the first detection frequency 1 / L is obtained 1 , the second detection cycle obtains the second detection frequency 1 / L 2 ;​​

[0023] Using the formula: V 1 =a 1 ×[(1 / L 1 ) / (1 / L 2 )×(1 / L 1 ) / (M / L 3 )]+a 2 ×[|v 1 -v 2 | / |v 2 -v 3 |] max

[0024] Calculate the soil index stability rate V corresponding to the heterofrequency detection area to be analyzed 1 , where L 3 represents the interval length of the target coordinate point corresponding to the life cycle record before detection, and M represents the total number of soil quality detection events recorded in the corresponding interval length; a 1 、a 2 Indicates the corresponding reference coefficient; v 1 Indicates the target data value of the soil quality detection event recorded in the second detection cycle in the heterofrequency detection area to be analyzed, v 2 Indicates the target data value of the soil quality detection event recorded in the second detection cycle of the heterofrequency detection area to be analyzed, v 3 Indicates the target data value recorded at the target coordinate point;

[0025] Set the soil index stability rate threshold V 0 , extract V 1 ≤V 0 The corresponding hetero-frequency detection area to be analyzed is the target detection area;

[0026] Data association storage of the second detection area to be analyzed refers to extracting the detection frequency of the second detection area to be analyzed in historical soil quality detection events and storing it in association with the name of the plant species.

[0027] The smaller the soil index stability rate is, the greater the soil index fluctuation caused by the phased frequency reduction in the hetero-frequency to-be-analyzed detection area with different detection frequencies is;

[0028] Further, step S300 includes the following:

[0029] Step S310: The plant remote sensing analysis data includes the reflectance NIR of the near-red band reflected by the vegetation and the reflectance R of the red band reflected by the vegetation required for the calculation of the normalized vegetation index. The real-time normalized vegetation index NDVI of the target detection area based on the target coordinate point in the life cycle before the detection is calculated using the plant remote sensing analysis data; NDVI=(NIR-R) / (NIR+R);

[0030] Step S320: Taking the adjacent soil quality detection events recorded in the target detection area as analysis units, analyzing the normalized vegetation index change rate D of each analysis unit from the target coordinate point forward 0 , D 0 =(NDVI max -NDVI min ) / NDVI max , and calculate the real-time normalized vegetation index change rate D in each analysis unit 1 , D 1 =|NDVI 1 -NDVI 2 | / NDVI 1 , where NDVI max Indicates the maximum value of the Normalized Difference Vegetation Index recorded in each analysis unit, NDVI min Indicates the minimum value of the Normalized Difference Vegetation Index recorded in each analysis unit; NDVI 1 Represents the normalized difference vegetation index recorded in any analysis unit at the previous moment, NDVI 2 It represents the normalized vegetation index recorded at the next moment in any analysis unit;

[0031] Step S330: Select D 0 -D 1 The extraction time of the corresponding analysis unit whose difference is less than the difference threshold is the abnormal growth node.

[0032] Further, step S400 includes the following:

[0033] Step S410: construct a response cycle with the abnormal growth node as the end node and the first detection time recorded before the abnormal growth node as the start node, extract the target data value recorded in the response cycle, and calculate the soil index abnormal rate U based on the target data value 1 , and taking the response period as an analysis unit, calculate the normalized vegetation index change rate D within the analysis unit 0 ;

[0034] Step S420: Extract the interval length L of the response cycle 0 , calculate the response detection frequency 1 / L 0 , if 1 / L 0 If the interval between the previous adjacent soil quality detection events is greater than or equal to the time interval between the previous adjacent soil quality detection events, the output detection frequency adjustment response model is W, W={w=L 0 |U 1 >U 1 ,D 0 >D 0}; U 1 Indicates the soil index change rate calculated in real time, D0 Indicates the normalized vegetation index change rate of the real-time calculation analysis unit,

[0035] If 1 / L 0 If it is less than the interval duration of the previous adjacent soil quality detection events, the interval duration of the previous adjacent soil quality detection events will still be output as the real-time detection frequency response duration for reminder.

[0036] A garden soil quality detection system based on multi-source data, the system includes a soil area division module, a detection database construction module, a target detection area analysis module, an association storage module, an abnormal growth node determination module, a response model construction module and a real-time detection module;

[0037] The soil area division module is used to divide the soil detection areas in the monitored garden based on the differences in plant species;

[0038] The detection database construction module is used to input the soil quality detection events within the planting cycle of each soil detection area history record into the detection system to form a detection database;

[0039] The target detection area analysis module is used to perform verification analysis on the first detection area to be analyzed to obtain a target detection area where the detection frequency affects the optimization processing operation;

[0040] The associated storage module is used to perform data associated storage on the second detection area to be analyzed;

[0041] The abnormal growth node determination module is used to determine the abnormal growth nodes of plants in the target detection area based on each soil quality detection event;

[0042] The response model building module is used to build a detection frequency adjustment response model for plants in the target detection area;

[0043] The real-time detection module is used to transmit the corresponding signal to the detection system for frequency adjustment when there is real-time monitoring of the same type of plants that meet the corresponding detection frequency adjustment response model.

[0044] Further, the target detection area analysis module includes a detection area generation unit to be analyzed, a detection area marking unit to be analyzed, a control detection area output unit, a soil index change rate calculation unit, a change discrete value calculation unit and a soil index stability rate calculation unit;

[0045] The to-be-analyzed detection region generating unit is used to generate a first to-be-analyzed detection region and a second to-be-analyzed detection region;

[0046] The to-be-analyzed detection area marking unit is used to mark the analysis interval duration of the first to-be-analyzed detection area as a same-frequency to-be-analyzed detection area and a different-frequency to-be-analyzed detection area;

[0047] The control detection area output unit is used to extract the first detection area to be analyzed corresponding to the detection system recording the same life cycle length and the ratio similarity of the life cycle length before detection to the life cycle length after detection is greater than the similarity threshold as the control detection area;

[0048] The soil index change rate calculation unit is used to calculate the soil index change rate of the same frequency detection area to be analyzed;

[0049] The abnormal discrete value calculation unit is used to calculate the soil index abnormal discrete value corresponding to the same frequency to be analyzed detection area; and extract the same frequency to be analyzed detection area corresponding to the discrete value less than the discrete value threshold as the target detection area;

[0050] The soil index stability rate calculation unit is used to calculate the soil index stability rate corresponding to the heterofrequency detection area to be analyzed, and extract the heterofrequency detection area to be analyzed whose soil index stability rate is less than or equal to a threshold as the target detection area.

[0051] Furthermore, the abnormal growth node determination module includes a normalized vegetation index calculation unit, a vegetation index change rate analysis unit and a node output unit;

[0052] The normalized vegetation index calculation unit is used to calculate the real-time normalized vegetation index NDVI of the target detection area based on the target coordinate point in the life cycle before detection by using the plant remote sensing analysis data;

[0053] The vegetation index change rate analysis unit is used to analyze the normalized vegetation index change rate of each analysis unit from the target coordinate point forward; and calculate the real-time normalized vegetation index change rate in each analysis unit;

[0054] The node output unit is used to select the extraction time of the corresponding analysis unit when the difference between the normalized vegetation index and the real-time normalized vegetation index change rate is less than the difference threshold as the abnormal growth node.

[0055] Further, the response model construction module includes a response cycle construction unit and a response model output unit;

[0056] The response cycle construction unit is used to construct a response cycle with the abnormal growth node as the end node and the first detection time recorded before the abnormal growth node as the start node.

[0057] The response model output unit is used to extract the interval duration of the response cycle and calculate the response detection frequency. If the response detection frequency is greater than or equal to the interval duration of the previous adjacent soil quality detection event, the corresponding detection frequency adjustment response model is output.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. The present invention monitors the planting soil corresponding to different plants in the garden by dividing the area, so that the variables of soil monitoring are controllable, and the corresponding search can be effectively performed based on the plant species when matching the corresponding model in the subsequent process;

[0060] 2. The present invention determines the soil anomalies affected by the detection frequency through the soil quality detection events recorded in the history, thereby analyzing and determining the abnormal nodes corresponding to the detection frequency reminders that may exist in each type of monitoring area, and combining the remote sensing image data analysis of the corresponding plants in each type of monitoring area to realize the construction of the detection frequency early warning model, thereby realizing the intelligent adjustment of the detection frequency based on the comprehensive analysis of soil data and remote sensing data; timely and effectively discovering the growth problems of plants, and improving the application efficiency of soil moisture monitoring stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 The present invention is a structural schematic diagram of a garden soil quality detection system based on multi-source data. DETAILED DESCRIPTION

[0062] 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.

[0063] Example: Figure 1 As shown, the present invention provides a garden soil quality detection system and method technical solution based on multi-source data, a garden soil quality detection method based on multi-source data, and the detection method comprises the following steps:

[0064] Step S100: Divide the monitored garden into soil detection areas based on the types of planted plants, with each soil detection area representing a unique type of plant; input the soil quality detection events of each soil detection area during the planting cycle into the detection system to form a detection database;

[0065] Step S200: Based on the detection database, the soil detection area corresponding to the soil optimization treatment operation recorded in the monitored garden is selected as the first detection area to be analyzed, and the soil detection area corresponding to the soil optimization treatment operation not recorded is the second detection area to be analyzed; the first detection area to be analyzed is verified and analyzed to obtain the target detection area where the detection frequency affects the optimization treatment operation; the second detection area to be analyzed is stored in data association;

[0066] Step S300: extracting plant remote sensing analysis data stored in the target detection area using remote sensing technology, and determining abnormal growth nodes of plants in the target detection area based on various soil quality detection events;

[0067] Step S400: Acquire soil detection data and plant remote sensing analysis data corresponding to the abnormal growth node, and construct a detection frequency adjustment response model for plants in the target detection area;

[0068] Step S500: When there are plants of the same type monitored in real time that meet the corresponding detection frequency adjustment response model, a corresponding signal is transmitted to the detection system for frequency adjustment.

[0069] Step S200 includes the following specific steps:

[0070] Soil optimization treatment operation refers to the operation corresponding to the treatment and restoration of soil after soil quality testing; if the soil quality test results show that the soil is compacted, the soil organic matter content is low, the soil is acidified, etc., then a series of treatment operations need to be carried out on the soil;

[0071] Extract the detection time of the soil quality detection event recorded in the first detection area to be analyzed, and calculate the interval time T between two adjacent detection events based on the detection time 1 , will record the interval time T of all soil quality detection events generated 1 If the frequencies are the same, the first detection area to be analyzed is marked as the same-frequency detection area to be analyzed, otherwise it is marked as the different-frequency detection area to be analyzed;

[0072] All soil quality detection events in the same-frequency detection area to be analyzed are recorded in chronological order to generate a same-frequency time axis sequence, and the detection data of each soil quality detection event is recorded with the detection time as the coordinate point; the coordinate point corresponding to the detection time of the soil optimization treatment operation is marked as the target coordinate point, and the life cycle of the corresponding type of plant is divided into the pre-detection life cycle and the post-detection life cycle with the target coordinate point as the segmentation point;

[0073] The first detection area to be analyzed corresponding to the same life cycle length recorded by the detection system and the ratio of the life cycle length before detection to the life cycle length after detection being greater than the similarity threshold is extracted as the control detection area;

[0074] Extract the test data recorded in the same frequency detection area to be analyzed. The test data refers to the soil data transmitted by the real-time soil moisture monitoring system. Determine the soil data for the soil optimization operation in the same frequency detection area to be analyzed as the target data. Use the formula: U 1 =[|u 1 -u 0 | / |u 2 -u 1 |]max ,

[0075] Calculate the soil index change rate U of the same frequency area to be analyzed 1 , where u 0 Indicates the target data value recorded in the soil quality detection event in the same frequency detection area to be analyzed, u 1 Indicates the target data value of the first adjacent detection event record in the life cycle before the soil quality detection event, u 2 Indicates record u 1 The target data value of the first adjacent detection event record in the life cycle before the detection; [|u 1 -u 0 | / |u 2 -u 1 |] max Indicates that when there are multiple target data types, the change rate of the target data type with the largest calculation ratio is selected as the output value;

[0076] The soil index change rate calculation formula is used to calculate the test data in the control test area to obtain the corresponding soil index change rate. The formula is: Q={[∑(UU 0 ) 2 ] / N} 1 / 2 ; Calculate the soil index anomaly discrete value Q corresponding to the same frequency detection area to be analyzed, where U 0 It represents the average value of soil index change rate of the corresponding same-frequency detection area to be analyzed and the control detection area, and N represents the total number of soil index change rates;

[0077] Set the soil index abnormal discrete value threshold Q 0 , extract Q 0 The corresponding same-frequency detection area to be analyzed is the target detection area.

[0078] Q 0 This means that when comparing with historical data, the soil data recorded by the soil moisture real-time monitoring system for plants corresponding to similar detection areas at abnormal time nodes are similar in fluctuation, and the impact of unreasonable detection frequency on data fluctuation similarity is higher. Unreasonable detection frequency means that the discovery of soil anomalies is not timely after the soil detection event occurs, and the setting of detection frequency does not effectively assist in soil environmental monitoring, resulting in the deterioration of the growth status of the planted plants.

[0079] Step S200 also includes the following specific steps:

[0080] ​​All soil quality detection events recorded in the hetero-frequency detection area to be analyzed are generated into a hetero-frequency time axis sequence in chronological order, and the target coordinate points in the hetero-frequency time axis sequence, as well as the life cycle before detection and the life cycle after detection are marked;

[0081] The interval between the detection time closest to the target coordinate point in the life cycle before the detection is constructed as the first detection period L 1 , the adjacent detection time of the first detection cycle in the life cycle before detection constitutes the second detection cycle L 2 ; Based on the first detection cycle, the first detection frequency 1 / L is obtained 1 , the second detection cycle obtains the second detection frequency 1 / L 2 ;

[0082] Using the formula: V 1 =a 1 ×[(1 / L 1 ) / (1 / L 2 )×(1 / L 1 ) / (M / L 3 )]+a 2 ×[|v 1 -v 2 | / |v 2 -v 3 |] max

[0083] Calculate the soil index stability rate V corresponding to the heterofrequency detection area to be analyzed 1 , where L 3 represents the interval length of the target coordinate point corresponding to the life cycle record before detection, and M represents the total number of soil quality detection events recorded in the corresponding interval length; a 1 、a 2 Indicates the corresponding reference coefficient; v 1 Indicates the target data value of the soil quality detection event recorded in the second detection cycle in the heterofrequency detection area to be analyzed, v 2 Indicates the target data value of the soil quality detection event recorded in the second detection cycle of the heterofrequency detection area to be analyzed, v 3 Indicates the target data value recorded at the target coordinate point;

[0084] Set the soil index stability rate threshold V 0 , extract V 1 ≤V 0 The corresponding hetero-frequency detection area to be analyzed is the target detection area;

[0085] Data association storage of the second detection area to be analyzed refers to extracting the detection frequency of the second detection area to be analyzed in historical soil quality detection events and storing it in association with the name of the plant species.

[0086] The smaller the soil index stability rate is, the greater the soil index fluctuation caused by the phased frequency reduction in the hetero-frequency to-be-analyzed detection area with different detection frequencies is;

[0087] As shown in the embodiment: To set the soil index stability rate threshold, it is necessary to set [(1 / L 1 ) / (1 / L 2 )×(1 / L 1 ) / (M / L 3 )] The threshold of the corresponding part is 1, that is, the first detection cycle is the same as the second detection cycle, 1 / L 1 =M / L 3 ; and [|v 1 -v 2 | / |v 2 -v 3 |] max The threshold needs to be set to at least 0.8. The soil index stability rate reflects the fluctuation of soil data reflected in the detection events recorded in the detection area to be analyzed as the detection frequency decreases.

[0088] Step S300 includes the following:

[0089] Step S310: The plant remote sensing analysis data includes the reflectance NIR of the near-red band reflected by the vegetation and the reflectance R of the red band reflected by the vegetation required for the calculation of the normalized vegetation index. The real-time normalized vegetation index NDVI of the target detection area based on the target coordinate point in the life cycle before the detection is calculated using the plant remote sensing analysis data; NDVI=(NIR-R) / (NIR+R);

[0090] Step S320: Taking the adjacent soil quality detection events recorded in the target detection area as analysis units, analyzing the normalized vegetation index change rate D of each analysis unit from the target coordinate point forward 0 , D 0 =(NDVI max -NDVI min ) / NDVI max , and calculate the real-time normalized vegetation index change rate D in each analysis unit 1 , D 1 =|NDVI 1 -NDVI 2 | / NDVI 1 , where NDVI max Indicates the maximum value of the Normalized Difference Vegetation Index recorded in each analysis unit, NDVI min Indicates the minimum value of the Normalized Difference Vegetation Index recorded in each analysis unit; NDVI 1Represents the normalized difference vegetation index recorded in any analysis unit at the previous moment, NDVI 2 It represents the normalized vegetation index recorded at the next moment in any analysis unit;

[0091] Step S330: Select D 0 -D 1 The extraction time of the corresponding analysis unit whose difference is less than the difference threshold is the abnormal growth node.

[0092] Step S400 includes the following:

[0093] Step S410: construct a response cycle with the abnormal growth node as the end node and the first detection time recorded before the abnormal growth node as the start node, extract the target data value recorded in the response cycle, and calculate the soil index abnormal rate U based on the target data value 1 , and taking the response period as an analysis unit, calculate the normalized vegetation index change rate D within the analysis unit 0 ;

[0094] Step S420: Extract the interval length L of the response cycle 0 , calculate the response detection frequency 1 / L 0 , if 1 / L 0 If the interval between the previous adjacent soil quality detection events is greater than or equal to the time interval between the previous adjacent soil quality detection events, the output detection frequency adjustment response model is W, W={w=L 0 |U 1 >U 1 ,D 0 >D 0}; U 1 Indicates the soil index change rate calculated in real time, D 0 represents the normalized vegetation index change rate of the real-time calculation and analysis unit. The real-time calculation and analysis unit here can form an analysis unit calculation with any two adjacent time intervals to achieve real-time dynamic monitoring; where U 1 The calculation is to use the real-time soil detection data as u0. This model shows that when the real-time calculated U 1 and D 0 Meet U 1 >U 1 And D 0 >D 0 When the interval length is L0, the reminder is executed to perform the soil quality detection signal;

[0095] If 1 / L 0 If it is less than the interval duration of the previous adjacent soil quality detection events, the interval duration of the previous adjacent soil quality detection events will still be output as the real-time detection frequency response duration for reminder.

[0096] This application is for the early warning response of the detection frequency when the frequency is reduced. When there is a large fluctuation in the soil detection data, the detection frequency is appropriately increased. Then, in the corresponding treatment plan, if the loss of a certain element content is large, it means a large change. Increasing the detection frequency can effectively improve the status monitoring of the changes in nutrient content of plants in the corresponding stage, ensure the healthy growth of plants, and provide a scientific basis for plant management. The reason for not monitoring when the frequency increases in soil detection events is that the increase in frequency can better realize the monitoring of the soil without affecting the growth status of plants.

[0097] A garden soil quality detection system based on multi-source data, the system includes a soil area division module, a detection database construction module, a target detection area analysis module, an association storage module, an abnormal growth node determination module, a response model construction module and a real-time detection module;

[0098] The soil area division module is used to divide the soil detection areas in the monitored garden based on the differences in plant species;

[0099] The detection database construction module is used to input the soil quality detection events within the planting cycle of each soil detection area history record into the detection system to form a detection database;

[0100] The target detection area analysis module is used to perform verification analysis on the first detection area to be analyzed to obtain a target detection area where the detection frequency affects the optimization processing operation;

[0101] The associated storage module is used to perform data associated storage on the second detection area to be analyzed;

[0102] The abnormal growth node determination module is used to determine the abnormal growth nodes of plants in the target detection area based on each soil quality detection event;

[0103] The response model building module is used to build a detection frequency adjustment response model for plants in the target detection area;

[0104] The real-time detection module is used to transmit the corresponding signal to the detection system for frequency adjustment when there is real-time monitoring of the same type of plants that meet the corresponding detection frequency adjustment response model.

[0105] The target detection area analysis module includes a detection area generation unit to be analyzed, a detection area marking unit to be analyzed, a control detection area output unit, a soil index change rate calculation unit, a change discrete value calculation unit and a soil index stability rate calculation unit;

[0106] The to-be-analyzed detection region generating unit is used to generate a first to-be-analyzed detection region and a second to-be-analyzed detection region;

[0107] The to-be-analyzed detection area marking unit is used to mark the analysis interval duration of the first to-be-analyzed detection area as a same-frequency to-be-analyzed detection area and a different-frequency to-be-analyzed detection area;

[0108] The control detection area output unit is used to extract the first detection area to be analyzed corresponding to the detection system recording the same life cycle length and the ratio similarity of the life cycle length before detection to the life cycle length after detection is greater than the similarity threshold as the control detection area;

[0109] The soil index change rate calculation unit is used to calculate the soil index change rate of the same frequency detection area to be analyzed;

[0110] The abnormal discrete value calculation unit is used to calculate the soil index abnormal discrete value corresponding to the same frequency to be analyzed detection area; and extract the same frequency to be analyzed detection area corresponding to the discrete value less than the discrete value threshold as the target detection area;

[0111] The soil index stability rate calculation unit is used to calculate the soil index stability rate corresponding to the heterofrequency detection area to be analyzed, and extract the heterofrequency detection area to be analyzed whose soil index stability rate is less than or equal to a threshold as the target detection area.

[0112] The abnormal growth node determination module includes a normalized vegetation index calculation unit, a vegetation index change rate analysis unit and a node output unit;

[0113] The normalized vegetation index calculation unit is used to calculate the real-time normalized vegetation index NDVI of the target detection area based on the target coordinate point in the life cycle before detection by using the plant remote sensing analysis data;

[0114] The vegetation index change rate analysis unit is used to analyze the normalized vegetation index change rate of each analysis unit from the target coordinate point forward; and calculate the real-time normalized vegetation index change rate in each analysis unit;

[0115] The node output unit is used to select the extraction time of the corresponding analysis unit when the difference between the normalized vegetation index and the real-time normalized vegetation index change rate is less than the difference threshold as the abnormal growth node.

[0116] The response model building module includes a response cycle building unit and a response model output unit;

[0117] The response cycle construction unit is used to construct a response cycle with the abnormal growth node as the end node and the first detection time recorded before the abnormal growth node as the start node.

[0118] The response model output unit is used to extract the interval duration of the response cycle and calculate the response detection frequency. If the response detection frequency is greater than or equal to the interval duration of the previous adjacent soil quality detection event, the corresponding detection frequency adjustment response model is output.

[0119] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A garden soil quality detection method based on multi-source data, characterized in that: The detection method includes the following steps: Step S100: Divide the soil detection areas in the monitored garden based on the differences in the types of planted plants, with each soil detection area representing a unique type of plant; input the soil quality detection events during the historical planting cycle of each soil detection area into the detection system to form a detection database. Step S200: Based on the detection database, screen the soil detection areas corresponding to the recorded soil optimization treatment operations in the monitored garden as the first areas to be analyzed and detected, and the soil detection areas without recorded soil optimization treatment operations as the second areas to be analyzed and detected; conduct a verification analysis on the first areas to be analyzed and detected to obtain the target detection areas where the detection frequency affects the optimization treatment operations; perform data association storage on the second areas to be analyzed and detected. The step S200 includes the following specific steps: The soil optimization treatment operation refers to the operation corresponding to the need for treatment and repair after soil quality detection. Extract the detection time in the recorded soil quality detection events in the first areas to be analyzed and detected, calculate the interval duration T1 between two adjacent detection events based on the detection time, and mark the first areas to be analyzed and detected corresponding to the same interval duration T1 generated by all recorded soil quality detection events as the areas to be analyzed and detected with the same frequency, and vice versa as the areas to be analyzed and detected with different frequencies. Generate a time axis sequence with the same frequency for all the recorded soil quality detection events in the areas to be analyzed and detected with the same frequency in chronological order, and record the detection data of each soil quality detection event with the detection time as the coordinate point; mark the coordinate point corresponding to the detection time of the recorded soil optimization treatment operation as the target coordinate point, and divide the life cycle of the corresponding type of plant into the pre-detection life cycle and the post-detection life cycle with the target coordinate point as the segmentation point. Extract the first areas to be analyzed and detected corresponding to the same life cycle length recorded in the detection system and with the similarity ratio of the pre-detection life cycle length to the post-detection life cycle length greater than the similarity threshold as the control detection areas. Extract the test data recorded in the same frequency detection area to be analyzed. The test data refers to the soil data transmitted by the real-time soil moisture monitoring system. Determine the soil data for the soil optimization operation in the same frequency detection area to be analyzed as the target data. Use the formula: U1=[|u1-u0| / |u2-u1|] max , calculate the soil index change rate U1 of the same-frequency detection area to be analyzed, where u0 represents the target data value recorded in the soil quality detection event of the same-frequency detection area to be analyzed, u1 represents the target data value recorded in the first adjacent detection event in the life cycle before the soil quality detection event, and u2 represents the target data value recorded in the first adjacent detection event in the life cycle before the detection when u1 is recorded; [|u1-u0| / |u2-u1|] max Indicates that when there are multiple target data types, the change rate of the target data type with the largest calculation ratio is selected as the output value; The soil index change rate calculation formula is used to calculate the test data in the control test area to obtain the corresponding soil index change rate. The formula is: Q={[∑(U-U0) 2 ] / N} 1 / 2 ; Calculate the soil index change discrete value Q corresponding to the same frequency detection area to be analyzed, where U0 represents the average value of the soil index change rate corresponding to the same frequency detection area to be analyzed and the control detection area, and N represents the total number of soil index change rates; Set the threshold Q0 for the discrete value of the soil index anomaly, and extract the areas to be analyzed and detected with the same frequency corresponding to Q < Q0 as the target detection areas. Step S300: Extract the plant remote sensing analysis data stored using remote sensing technology in the target detection areas, and determine the abnormal growth nodes of the plants in the target detection areas based on each soil quality detection event. Step S400: Obtain the soil detection data and plant remote sensing analysis data corresponding to the abnormal growth nodes, and construct a detection frequency adjustment response model for the plants in the target detection areas. Step S500: When there are real-time monitored plants of the same type that meet the corresponding detection frequency adjustment response model, transmit the corresponding signal to the detection system for frequency adjustment.

2. A garden soil quality detection method based on multi-source data according to claim 1, characterized in that: The step S200 also includes the following specific steps: Generate a time axis sequence with different frequencies for all the recorded soil quality detection events in the areas to be analyzed and detected with different frequencies in chronological order, and at the same time mark the target coordinate points, as well as the pre-detection life cycle and the post-detection life cycle, in the time axis sequence with different frequencies. The interval between the detection time closest to the target coordinate point in the pre-detection life cycle and the target coordinate point is constructed as the first detection period L1, and the adjacent detection times of the first detection period in the pre-detection life cycle constitute the second detection period L2; the first detection frequency 1 / L1 is obtained based on the first detection period, and the second detection frequency 1 / L2 is obtained in the second detection period; Using the formula: V1 = a1 × [(1 / L1) / (1 / L2) × (1 / L1) / (M / L3)] + a2 × [|v1-v2| / |v2-v3|] max Calculate the soil index stability rate V1 corresponding to the heterofrequency detection area to be analyzed, where L3 represents the interval length of the life cycle record before the detection corresponding to the target coordinate point, M represents the total number of soil quality detection events recorded in the corresponding interval length; a1 and a2 represent the corresponding reference coefficients; v1 represents the target data value of the soil quality detection event recorded in the second detection cycle of the heterofrequency detection area to be analyzed, v2 represents the target data value of the soil quality detection event recorded in the heterofrequency detection area to be analyzed after the second detection cycle, and v3 represents the target data value recorded at the target coordinate point; Set the soil index stability rate threshold V0, and extract the heterofrequency detection area to be analyzed corresponding to V1≤V0 as the target detection area; The data association storage of the second detection area to be analyzed refers to extracting the detection frequency of the second detection area to be analyzed in historical soil quality detection events and storing it in association with the plant species name.

3. The garden soil quality detection method based on multi-source data according to claim 1 is characterized in that: The step S300 includes the following: Step S310: The plant remote sensing analysis data includes the reflectance NIR of the near-red band reflected by the vegetation and the reflectance R of the red band reflected by the vegetation required for the calculation of the normalized vegetation index, and the real-time normalized vegetation index NDVI of the target detection area based on the target coordinate point in the life cycle before the detection is calculated using the plant remote sensing analysis data; NDVI=(NIR-R) / (NIR+R); Step S320: Taking the adjacent soil quality detection events recorded in the target detection area as the analysis unit, analyzing the normalized vegetation index change rate D0 of each analysis unit from the target coordinate point forward, D0=(NDVI max -NDVI min ) / NDVI max , and calculate the real-time normalized difference vegetation index change rate D1 in each analysis unit, D1 = |NDVI1-NDVI2| / NDVI1, where NDVI max Indicates the maximum value of the Normalized Difference Vegetation Index recorded in each analysis unit, NDVI min It represents the minimum value of the normalized difference vegetation index recorded in each analysis unit; NDVI1 represents the normalized vegetation index recorded at the previous moment in any analysis unit, and NDVI2 represents the normalized vegetation index recorded at the next moment in any analysis unit; Step S330: Select the extraction time of the analysis unit corresponding to the difference between D0 and D1 that is less than the difference threshold as the abnormal growth node.

4. The garden soil quality detection method based on multi-source data according to claim 1 is characterized in that: The step S400 includes the following: Step S410: construct a response cycle with the abnormal growth node as the end node and the first detection time recorded before the abnormal growth node as the start node, extract the target data value recorded in the response cycle, and calculate the soil index abnormal rate U1 based on the target data value, and take the response cycle as an analysis unit to calculate the normalized vegetation index change rate D0 within the analysis unit; Step S420: extract the interval duration L0 of the response cycle, calculate the response detection frequency 1 / L0, if 1 / L0 is greater than or equal to the interval duration of the previous adjacent soil quality detection event, then output the detection frequency adjustment response model as W, W={w=L0|U 1 >U1,D 0 >D0};U 1 Indicates the soil index change rate calculated in real time, D 0 Indicates the normalized vegetation index change rate of the real-time calculation analysis unit, If 1 / L0 is less than the interval duration between the previous adjacent soil quality detection events, the interval duration between the previous adjacent soil quality detection events is still output as the real-time detection frequency response duration for reminder.

5. A garden soil quality detection system based on multi-source data, using a garden soil quality detection method based on multi-source data according to any one of claims 1 to 4, characterized in that: The system includes a soil area division module, a detection database construction module, a target detection area analysis module, an association storage module, an abnormal growth node determination module, a response model construction module and a real-time detection module; The soil area division module is used to divide the soil detection areas in the monitored garden based on the differences in plant species; The detection database construction module is used to input soil quality detection events within the planting cycle of each soil detection area history into the detection system to form a detection database; The target detection area analysis module is used to perform verification analysis on the first detection area to be analyzed to obtain a target detection area where the detection frequency affects the optimization processing operation; The association storage module is used to perform data association storage on the second detection area to be analyzed; The abnormal growth node determination module is used to determine the abnormal growth nodes of plants in the target detection area based on each soil quality detection event; The response model building module is used to build a detection frequency adjustment response model for plants in the target detection area; The real-time detection module is used to transmit a corresponding signal to the detection system for frequency adjustment when there is real-time monitoring of the same type of plants that meet the corresponding detection frequency adjustment response model.

6. The garden soil quality detection system based on multi-source data according to claim 5 is characterized by: The target detection area analysis module includes a detection area generation unit to be analyzed, a detection area marking unit to be analyzed, a control detection area output unit, a soil index change rate calculation unit, a change discrete value calculation unit and a soil index stability rate calculation unit; The to-be-analyzed detection region generating unit is used to generate a first to-be-analyzed detection region and a second to-be-analyzed detection region; The to-be-analyzed detection area marking unit is used to mark the analysis interval duration of the first to-be-analyzed detection area as a same-frequency to-be-analyzed detection area and a different-frequency to-be-analyzed detection area; The control detection area output unit is used to extract the first detection area to be analyzed corresponding to the detection system recording the same life cycle length and the ratio of the life cycle length before detection to the life cycle length after detection being greater than the similarity threshold as the control detection area; The soil index change rate calculation unit is used to calculate the soil index change rate of the same frequency detection area to be analyzed; The abnormal discrete value calculation unit is used to calculate the soil index abnormal discrete value corresponding to the same frequency detection area to be analyzed; And extract the same frequency detection area to be analyzed corresponding to the discrete value less than the discrete value threshold as the target detection area; The soil index stability rate calculation unit is used to calculate the soil index stability rate corresponding to the heterofrequency detection area to be analyzed, and extract the heterofrequency detection area to be analyzed whose soil index stability rate is less than or equal to a threshold as the target detection area.

7. The garden soil quality detection system based on multi-source data according to claim 6 is characterized by: The abnormal growth node determination module includes a normalized vegetation index calculation unit, a vegetation index change rate analysis unit and a node output unit; The normalized vegetation index calculation unit is used to calculate the real-time normalized vegetation index NDVI of the target detection area based on the target coordinate point in the pre-detection life cycle using the plant remote sensing analysis data; The vegetation index change rate analysis unit is used to analyze the normalized vegetation index change rate of each analysis unit from the target coordinate point forward; And calculate the real-time normalized difference vegetation index change rate in each analysis unit; The node output unit is used to select the extraction moment of the corresponding analysis unit when the difference between the normalized vegetation index and the real-time normalized vegetation index change rate is less than the difference threshold as the abnormal growth node.

8. The garden soil quality detection system based on multi-source data according to claim 6 is characterized by: The response model building module includes a response cycle building unit and a response model output unit; The response cycle construction unit is used to construct a response cycle with the abnormal growth node as the end node and the first detection time recorded before the abnormal growth node as the start node. The response model output unit is used to extract the interval duration of the response cycle and calculate the response detection frequency. If the response detection frequency is greater than or equal to the interval duration of the previous adjacent soil quality detection event, the corresponding detection frequency adjustment response model is output.

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