Remote automatic management system and method for lotus root field cultivation

Abstract

The invention discloses a remote automatic management system and method for lotus root field cultivation, and relates to the technical field of cultivation automatic management. Reflected wave data of a sludge layer is obtained through an underwater ultrasonic array; constructing a three-dimensional density field according to the echo delay time in the time domain feature and the dominant frequency attenuation rate in the frequency domain feature; when the density gradient value exceeds a preset threshold value and the duration reaches a critical value, determining a hardened risk area, and generating soil loosening path planning data; and sending the soil loosening path planning data to the water surface unmanned ship, and triggering the soil loosening unmanned aerial vehicle to execute soil loosening operation according to the planning path. The hardening area can be recognized in real time, the monitoring precision is improved, the soil loosening path planning data is generated based on the density gradient value and the oxidation-reduction potential data, and the water surface unmanned ship and the soil loosening unmanned aerial vehicle are adopted for cooperative operation, so that accurate soil loosening is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of automated cultivation management, and particularly to a remote automated management system and method for field cultivation of lotus roots. Background Art

[0002] As an important aquatic economic crop, the cultivation process of lotus roots has relatively high requirements for soil environment, water quality conditions, and nutrient supply. At present, the cultivation of lotus roots mainly relies on manual management, involving multiple links such as water body regulation, soil loosening, fertilization, and disease monitoring. However, the traditional management model has the following technical bottlenecks:

[0003] The growth environment of lotus roots is mainly the underwater silt layer, and its physical and chemical properties (such as density gradient, pH value, redox potential, etc.) directly affect crop growth. Existing field management methods mostly rely on manual experience and lack efficient and real-time soil condition monitoring means, making it difficult to accurately judge the degree of silt compaction and its impact on the growth of lotus root roots.

[0004] Therefore, in view of the above technical problems, the present invention proposes a remote automated management method and system for field cultivation of lotus roots based on underwater ultrasonic detection, three-dimensional density field construction, intelligent soil loosening path planning, and dynamic fertilization regulation. Summary of the Invention

[0005] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title, but such simplifications or omissions shall not be used to limit the scope of the present invention.

[0006] In view of the problems existing in the above-mentioned prior art, the present invention is proposed.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] A remote automated management method for field cultivation of lotus roots, comprising:

[0009] Step 1: Obtain the reflected wave data of the silt layer through an underwater ultrasonic array, and the reflected wave data includes time-domain characteristics and frequency-domain characteristics;

[0010] Step 2: Construct a three-dimensional density field according to the echo delay time in the time-domain characteristics and the main frequency attenuation rate in the frequency-domain characteristics, and the three-dimensional density field includes density gradient values;

[0011] Step 3: When the density gradient value exceeds a preset threshold and the continuous duration reaches a critical value, it is determined as a compaction risk area, and soil loosening path planning data is generated;

[0012] Step 4: Send the loosening path planning data to the unmanned surface vessel, and trigger the loosening UAV to perform the loosening operation according to the planned path;

[0013] Step 5: Synchronously obtain the pH value and redox potential data of the compacted risk area, and calculate the organic matter injection amount in combination with the density gradient value;

[0014] Step 6: Control the solenoid valve opening of the pre-buried pipeline according to the organic matter injection amount, so that the organic matter diffuses along the pore distribution channel after loosening.

[0015] As a preferred solution of the remote automatic management method for lotus root field cultivation described in the present invention, wherein: the process of constructing the three-dimensional density field in step S2 is as follows:

[0016] S201: The time-domain feature is convolved with the preset absorption coefficient matrix through the echo peak intensity to obtain a preliminary density distribution;

[0017] S202: The frequency-domain feature generates a density correction factor through the spectral centroid offset amount through a matching algorithm;

[0018] S203: Multiply the preliminary density distribution by the density correction factor to obtain the final three-dimensional density field data.

[0019] As a preferred solution of the remote automatic management method for lotus root field cultivation described in the present invention, wherein: the calculation formula of the correction factor calculated by the matching algorithm is:

[0020]

[0021] where λ is an empirical parameter, f ref is the standard reference frequency, and Δf is the spectral centroid offset amount.

[0022] As a preferred solution of the remote automatic management method for lotus root field cultivation described in the present invention, wherein: the conditions for determining the compacted risk area in step S3 include:

[0023] When the density gradient value > the first threshold, initiate a primary warning;

[0024] When the density gradient value lasts for a set time and the density gradient change rate of adjacent grids > the set threshold percentage, it is marked as a high-risk area;

[0025] When generating the loosening path planning data, preferentially process the aggregated area containing more than one continuous high-risk area.

[0026] As a preferred solution of the remote automatic management method for lotus root field cultivation described in the present invention, wherein: the method for calculating the organic matter injection amount in step S5 includes:

[0027] Establish a compensation equation: Q = K·(G·S) / (R0 - R t ), where:

[0028] Q is the organic matter injection volume, and K is the equipment coefficient;

[0029] G is the density gradient value, and S is the pH sensitivity coefficient;

[0030] R0 is the ORP reference value, and R t is the real-time ORP value;

[0031] When (R0 - R t ) < the second threshold value, automatically switch to the emergency compensation mode and multiply the K value by the amplification factor for amplification.

[0032] As a preferred solution of the remote automatic management method for field cultivation of lotus roots described in the present invention, wherein: the method for controlling the solenoid valve opening degree in step S6 is:

[0033] According to the porosity distribution data after soil loosening, dynamically adjust the spacing D between adjacent injection ports. The calculation formula is:

[0034] D = D0×(1 - ΔG / G max ), where:

[0035] D0 is the initial spacing;

[0036] ΔG is the difference between the current density gradient value and the target value;

[0037] G max is the allowable maximum density gradient difference;

[0038] Among them, when ΔG > G max , trigger the secondary soil loosening operation and recalculate the D value.

[0039] As a preferred solution of the remote automatic management method for field cultivation of lotus roots described in the present invention, wherein: the method further includes an abnormal data processing step:

[0040] When the mutation rate of the ultrasonic reflection wave data exceeds the safety threshold, start the optical verification module; obtain the image of the silt layer through the underwater camera, and use the convolutional neural network to identify the biological disturbance characteristics; if the activity characteristics of frogs or snails are identified, freeze the update of the current density field data until the disturbance characteristics disappear.

[0041] A remote automatic management system applied to the above-mentioned remote automatic management method for field cultivation of lotus roots. The system includes:

[0042] Underwater ultrasonic detection module, which obtains the reflected wave data of the silt layer through an underwater ultrasonic array, including time-domain characteristics and frequency-domain characteristics;

[0043] Density field construction and analysis module, which constructs a three-dimensional density field based on the ultrasonic data, analyzes the density gradient value, and identifies the hardening risk area;

[0044] Hardening risk assessment and path planning module; it detects the density gradient value, combines the duration, determines the hardening risk area, and generates a soil loosening operation path;

[0045] Unmanned soil loosening operation module, which receives the soil loosening path data and controls the surface unmanned ship and the soil loosening unmanned aerial vehicle to perform the soil loosening task according to the planned path;

[0046] Environmental parameter monitoring module, which is used to monitor environmental data such as soil pH value and redox potential;

[0047] And the organic matter injection management module, which is used to calculate the organic matter injection amount according to the environmental data and the density gradient value, and dynamically adjust the spacing between adjacent injection ports according to the porosity distribution data after soil loosening to control the organic matter diffusion channel.

[0048] The present invention also discloses a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned remote automatic management method for lotus root field cultivation are realized.

[0049] The present invention also discloses a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned remote automatic management method for lotus root field cultivation are realized.

[0050] Advantages of the present invention:

[0051] 1. The present invention collects the reflected wave data of the silt layer through an underwater ultrasonic array, constructs a three-dimensional density field by combining time-domain and frequency-domain characteristics, can identify the hardening area in real time, improve the monitoring accuracy, generate soil loosening path planning data based on the density gradient value and redox potential data, and adopts the collaborative operation of a surface unmanned ship and a soil loosening unmanned aerial vehicle to achieve precise soil loosening. At the same time, by dynamically controlling the organic matter injection amount, the soil nutrient distribution is optimized, and the adaptability of the lotus root growth environment is improved.

[0052] 2. The present invention combines the optical verification module and the convolutional neural network technology, can perform anomaly detection on the ultrasonic data, avoid the influence of biological disturbance on the measurement accuracy, and thus improve the system stability. The present invention significantly improves the intelligent level of lotus root field management, reduces the labor cost, and improves the production efficiency and the sustainability of the farmland ecological environment. Description of the Drawings

[0053] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them:

[0054] Figure 1 It is a schematic diagram of the overall structure of a remote automatic management method for lotus root field cultivation proposed by the present invention. Specific embodiments

[0055] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be given in conjunction with the drawings in the specification.

[0056] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0057] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that excludes other embodiments.

[0058] Refer to Figure 1 , which is an embodiment of the present invention, providing a remote automatic management method for lotus root field cultivation. This method includes: Step 1: Obtain the reflected wave data of the silt layer through an underwater ultrasonic array. The reflected wave data includes time-domain characteristics and frequency-domain characteristics, providing basic data for subsequent density calculation and hardening risk determination. Through a high-precision ultrasonic array, the resolution of the echo signal can be effectively improved, ensuring the reliability of the data.

[0059] Step 1: Obtain the reflected wave data of the silt layer through an underwater ultrasonic array. The reflected wave data includes time-domain characteristics and frequency-domain characteristics, providing basic data for subsequent density calculation and hardening risk determination. Through a high-precision ultrasonic array, the resolution of the echo signal can be effectively improved, ensuring the reliability of the data.

[0060] Step 2: Construct a three-dimensional density field according to the echo delay time in the time-domain characteristics and the main frequency attenuation rate in the frequency-domain characteristics. The three-dimensional density field includes density gradient values.

[0061] Specifically: The process of constructing the three-dimensional density field is as follows:

[0062] S201: The time-domain features are convolved with a preset absorption coefficient matrix through the echo peak intensity to obtain a preliminary density distribution. The convolution calculation process is as follows:

[0063] Let I(t) be the intensity change of the echo signal over time, that is, the amplitude signal of the ultrasonic echo at time t; let M(x, y, z) be the preset absorption coefficient matrix, then the convolution calculation formula used is:

[0064]

[0065] Among them, the double summation operation in the above formula represents cumulative calculation within the entire time window (i dimension) and spatial region (j dimension), and D init (x, y, z) represents the preliminarily calculated three-dimensional density distribution, which is the silt density at the spatial position (x, y, z). The unit of the convolution calculation result is normalized according to specific physical quantities to make it have practical physical significance.

[0066] S202: The frequency-domain features generate a density correction factor through the spectral centroid offset using a matching algorithm.

[0067] Specifically:

[0068] Let Δf be the spectral centroid offset (i.e., the degree of change from the reference spectrum), then the calculation formula for the correction factor using the matching algorithm is:

[0069]

[0070] Among them, λ is an empirical parameter, and f ref is the standard reference frequency.

[0071] S203: Multiply the preliminary density distribution D init (x, y, z) by the density correction factor C(x, y, z) to obtain the final three-dimensional density field data D final . And the final density gradient value is a vector quantity, representing the rate of change of density with respect to spatial coordinates, usually calculated using the gradient operator as follows:

[0072]

[0073] Among them, represents the rate of change of density in the x direction;

[0074] represents the rate of change of density in the y direction;

[0075] Represents the rate of change of density in the z - direction.

[0076] Step 3: When the density gradient value exceeds the preset threshold and the duration reaches the critical value, it is determined as the hardening risk area, and the soil loosening path planning data is generated.

[0077] Specifically: When the density gradient value > the first threshold, the primary warning is initiated; for example, when the density gradient value G exceeds 0.15 g / cm 3 / m, the primary warning is initiated, and the spatial coordinates of this area are recorded. At this time, the soil loosening operation is not immediately triggered, but it enters the continuous monitoring state to observe the change trend of the density gradient value.

[0078] When the density gradient value lasts for the set time (for example: 6 hours and is still higher than 0.15 g / cm 3 / m) and the change rate of the density gradient of adjacent grids (ΔG / G) > the set threshold percentage (for example: 5%), it is marked as the high - risk area, which indicates that there may be serious hardening phenomena in this area and it needs to be key - processed.

[0079] When generating the soil loosening path planning data, the aggregated area containing more than 3 consecutive high - risk areas is preferentially processed to ensure the integrity and efficiency of the soil loosening operation.

[0080] It should be especially explained that: The soil loosening path planning data is generated based on the existing grid search method of the A* algorithm. Simply put, it is: Set the starting point as the nearest high - risk grid and search for the shortest path to adjacent grids; constrain the search direction to give priority to forward search to avoid the UAV hovering in the low - risk area; avoid path self - intersection to ensure smooth and efficient path planning.

[0081] Step 4: Send the soil loosening path planning data to the surface unmanned ship to trigger the soil loosening UAV to perform the soil loosening operation according to the planned path.

[0082] Step 5: Synchronously obtain the pH value and redox potential data of the hardening risk area, and calculate the organic matter injection amount in combination with the density gradient value.

[0083] Specifically: The method for calculating the organic matter injection amount in step S5 includes:

[0084] Establish the compensation equation: Q = K·(G·S) / (R0 - R t ), where:

[0085] Q is the organic matter injection amount, and K is the equipment coefficient;

[0086] G is the density gradient value (g / cm 3 / m), and S is the pH sensitivity coefficient;

[0087] R0 is the ORP reference value, and R tis the real-time ORP value;

[0088] It can be seen from the above formula that the greater the soil density gradient (the increase in G), the more serious the degree of soil compaction, and more organic matter is required to improve the soil structure; pH affects the solubility of organic matter and the decomposition efficiency of microorganisms, so S is introduced for compensation.

[0089] At the same time, ORP reflects the redox ability of the soil, and (R0 - R t ) is used as the denominator to ensure that the injection amount increases under low oxidation conditions (i.e., when R t is low) to accelerate soil restoration.

[0090] When (R0 - R t ) < the second threshold value, this situation indicates that the soil is in an extremely low oxidation state, and it automatically switches to the emergency compensation mode, increasing the K value to 1.2 times the reference value to accelerate the diffusion of organic matter and improve soil permeability.

[0091] S6. Control the opening degree of the solenoid valve of the pre-buried pipeline according to the injection amount of organic matter, so that the organic matter diffuses along the pore distribution channel after soil loosening. The specific method for controlling the opening degree of the solenoid valve is as follows:

[0092] First, through the ultrasonic array and the soil loosening path planning data, obtain the porosity distribution data after soil loosening, and the change in porosity directly affects the diffusion efficiency of organic matter.

[0093] According to the porosity distribution data after soil loosening, dynamically adjust the spacing D between adjacent injection ports. The calculation formula is:

[0094] D = D0 × (1 - ΔG / G max ), where:

[0095] D0 is the initial spacing;

[0096] ΔG is the difference between the current density gradient value and the target value;

[0097] G max is the maximum allowable density gradient difference;

[0098] In the above formula, the theoretical basis of this formula is that organic matter diffuses faster in areas with larger porosity, so the spacing between injection ports can be appropriately increased; while in areas with lower porosity, the diffusion is restricted, and the spacing between injection ports needs to be reduced to increase the local injection concentration to ensure uniform improvement effect.

[0099] To further optimize the distribution of organic matter, when ΔG > G max , trigger the secondary soil loosening operation and recalculate the D value. This ensures the continuity of soil density adjustment and prevents poor growth caused by uneven distribution of organic matter. The threshold setting refers to experimental data, and the typical setting is:

[0100] G max = 0.2 g / cm 3 / m (for highly viscous soil);

[0101] G max = 0.15 g / cm 3 / m (for medium viscous soil);

[0102] G max = 0.1 g / cm 3 / m (for low viscous soil).

[0103] In addition, it should be particularly noted that when abnormal data appears in step S1, the abnormal data needs to be processed, and the processing methods include:

[0104] When the mutation rate of ultrasonic reflection wave data exceeds the safety threshold, the optical verification module is activated. Its purpose is to prevent data misjudgment caused by short-term environmental factors or abnormal interference. For example, in heavy rain or strong wind weather, the drastic changes in the water surface and underwater environment may cause mutations in ultrasonic data, rather than actual changes in the density of the silt layer. Therefore, the optical verification module is used for cross-verification to improve the reliability of the data.

[0105] The image of the silt layer is obtained through an underwater camera, and a convolutional neural network is used to identify the characteristics of biological disturbance; if the activity characteristics of frogs or snails are identified, the update of the current density field data is frozen until the disturbance characteristics disappear. Its purpose is to use deep learning methods to process underwater camera data and extract key features, such as: dynamic changes in particle distribution, abnormal ripple patterns, etc. Specifically, a convolutional neural network (CNN) is used to extract features from the obtained images. The first layer of convolution is used to detect edge information, the second layer is used to identify specific disturbance patterns, and finally, the disturbance feature classification is performed through the fully connected layer. This method can effectively detect the minute disturbances caused by the activities of frogs and the crawling traces of snails in the silt layer.

[0106] To further fit the application scenario, the following is an example: Suppose in a certain paddy field area, the mutation rate of ultrasonic reflection wave data is 0.21 s -1 , while the safety threshold is set at 0.1 s -1 , then the optical verification module is triggered. The underwater camera captures a large number of small fluctuations, and the CNN identifies an 85% probability of frog activity. Therefore, the system freezes the update of the density field data for 30 minutes. After the frogs leave, the system automatically resumes data collection.

[0107] This embodiment also provides a remote automation management system for a remote automation management method applied to lotus root field cultivation. The system includes:

[0108] Underwater ultrasonic detection module, which obtains reflected wave data of the silt layer through an underwater ultrasonic array, including time-domain characteristics and frequency-domain characteristics; density field construction and analysis module, which constructs a three-dimensional density field according to the ultrasonic data, analyzes the density gradient value, and identifies the hardening risk area; hardening risk assessment and path planning module; detects the density gradient value, combines the duration, determines the hardening risk area, and generates a soil loosening operation path;

[0109] Unmanned soil loosening operation module, which receives soil loosening path data and controls the surface unmanned ship and the soil loosening drone to perform soil loosening tasks according to the planned path; environmental parameter monitoring module, which is used to monitor environmental data such as soil pH value and redox potential; and organic matter injection management module, which calculates the organic matter injection amount according to the environmental data and the density gradient value, and dynamically adjusts the spacing between adjacent injection ports according to the porosity distribution data after soil loosening to control the organic matter diffusion channel.

[0110] This embodiment also provides a computer device, which is applicable to a situation of a remote automation management method for lotus root field cultivation, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement a remote automation management method for lotus root field cultivation as proposed in the above embodiment.

[0111] The computer device can be a terminal. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad set on the shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0112] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the remote automatic management method for lotus root field cultivation proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM for short), electrically erasable programmable read-only memory (EEPROM for short), erasable programmable read-only memory (EPROM for short), programmable read-only memory (PROM for short), read-only memory (ROM for short), magnetic memory, flash memory, magnetic disk or optical disc.

[0113] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A remote automated management method for field cultivation of lotus roots, characterized in that, Including: Step 1: Obtain the reflected wave data of the silt layer through an underwater ultrasonic array, where the reflected wave data includes time-domain characteristics and frequency-domain characteristics; Step 2: Construct a three-dimensional density field based on the echo delay time in the time-domain characteristics and the main frequency attenuation rate in the frequency-domain characteristics. The three-dimensional density field includes density gradient values; Step 3: When the density gradient value exceeds a preset threshold and the continuous duration reaches a critical value, it is determined as a hardening risk area, and the loosening path planning data is generated; Step 4: Send the loosening path planning data to the unmanned surface vessel to trigger the loosening drone to perform the loosening operation according to the planned path; Step 5: Synchronously obtain the pH value and redox potential data of the hardening risk area, and calculate the organic matter injection amount in combination with the density gradient value; Step 6: Control the solenoid valve opening degree of the pre-buried pipeline according to the organic matter injection amount, so that the organic matter diffuses along the pore distribution channel after loosening.

2. The remote automatic management method for field cultivation of lotus roots according to claim 1, characterized in that: The process of constructing the three-dimensional density field in Step S2 is as follows: S201: The time-domain characteristics are convolved with a preset absorption coefficient matrix through the echo peak intensity to obtain a preliminary density distribution; S202: The frequency-domain characteristics generate a density correction factor through the spectral centroid offset by a matching algorithm; S203: Multiply the preliminary density distribution by the density correction factor to obtain the final three-dimensional density field data.

3. The remote automatic management method for lotus root field cultivation according to claim 2, characterized in that: The calculation formula for the correction factor calculated by the matching algorithm is: where λ is an empirical parameter, f ref is the standard reference frequency, and Δf is the spectral centroid offset.

4. The remote automatic management method for lotus root field cultivation according to claim 3, characterized in that: The conditions for determining the hardening risk area in Step S3 include: When the density gradient value > the first threshold, initiate a primary warning; When the density gradient value lasts for a set time and the density gradient change rate of adjacent grids > the set threshold percentage, it is marked as a high-risk area; When generating the loosening path planning data, preferentially process the aggregated area containing more than one continuous high-risk area.

5. The remote automatic management method for field cultivation of lotus roots according to claim 4, characterized in that: The method for calculating the organic matter injection amount in Step S5 includes: Establish a compensation equation: Q = K·(G·S) / (R0 - R t ), where: Q is the organic matter injection amount, and K is the equipment coefficient; G is the density gradient value, and S is the pH sensitivity coefficient; R0 is the ORP reference value, and R t is the real-time ORP value; When (R0 - R t ) is less than the second threshold value, automatically switch to the emergency compensation mode, and multiply the K value by the amplification factor for amplification.

6. The remote automated management method for lotus root field cultivation according to claim 5, characterized in that: When controlling the solenoid valve opening degree in Step S6: According to the porosity distribution data after loosening, dynamically adjust the spacing D between adjacent injection ports. The calculation formula is: D = D0 × (1 - ΔG / G max ), where: D0 is the initial spacing; ΔG is the difference between the current density gradient value and the target value; G max is the maximum allowable density gradient difference; Among them, when ΔG > G max a secondary soil loosening operation is triggered and the D value is recalculated.

7. The remote automatic management method for lotus root field cultivation according to claim 1, characterized in that: It also includes an abnormal data processing step: When the mutation rate of the ultrasonic reflected wave data exceeds the safety threshold, start the optical verification module; obtain the silt layer image through an underwater camera, and use a convolutional neural network to identify the biological disturbance characteristics; if the activity characteristics of frogs or snails are identified, freeze the update of the current density field data until the disturbance characteristics disappear.

8. A remote automated management system for field cultivation of lotus roots, which is applied to the remote automated management method for field cultivation of lotus roots described in any one of the above claims 1-7, and is characterized in that: This system includes: An underwater ultrasonic detection module that obtains the reflected wave data of the silt layer through an underwater ultrasonic array, including time-domain characteristics and frequency-domain characteristics; A density field construction and analysis module that constructs a three-dimensional density field based on the ultrasonic data, analyzes the density gradient value, and identifies the hardening risk area; A hardening risk assessment and path planning module; detects the density gradient value, combines the continuous duration, determines the hardening risk area, and generates the loosening operation path; An unmanned loosening operation module that receives the loosening path data and controls the unmanned surface vessel and the loosening drone to perform the loosening task according to the planned path; An environmental parameter monitoring module for monitoring environmental data such as soil pH value and redox potential; And an organic matter injection management module for calculating the amount of organic matter injection according to the environmental data and the density gradient value, and dynamically adjusting the spacing between adjacent injection ports according to the porosity distribution data after soil loosening to control the organic matter diffusion channel.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, the steps of a remote automatic management method for lotus root field cultivation according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of a remote automatic management method for lotus root field cultivation according to any one of claims 1 to 7 are implemented.

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