A remote automatic management system for lotus root field cultivation and a method thereof

Abstract

The application discloses a kind of lotus root field cultivation remote automation management system and method thereof, it is related to cultivation automation management technical field, by underwater ultrasonic wave array obtains the reflection wave data of silt layer;According to the echo delay time in the time domain feature and the main frequency attenuation rate in the frequency domain feature, construct three-dimensional density field;When the density gradient value exceeds preset threshold and duration reaches critical value, it is determined that the hardening risk area, and generates soil loosening path planning data;The soil loosening path planning data is sent to water unmanned ship, and triggers soil loosening unmanned plane to execute soil loosening operation according to planning path.The application can identify hardening area in real time, improve monitoring accuracy, generate soil loosening path planning data based on density gradient value and oxidation-reduction potential data, and adopt water unmanned ship and soil loosening unmanned plane cooperative operation, realize accurate soil loosening.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic management of cultivation, and particularly relates to a remote automatic management system and method for lotus root field cultivation. BACKGROUND

[0002] As an important aquatic economic crop, lotus root has high requirements for soil environment, water quality conditions and nutrient supply during cultivation. Currently, lotus root planting mainly relies on manual management, involving water regulation, soil loosening, fertilization and disease monitoring. However, the traditional management mode has the following technical bottlenecks:

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

[0004] Therefore, in view of the above technical problems, the present application provides a remote automatic management method and system for lotus root field cultivation based on underwater ultrasonic detection, three-dimensional density field construction, intelligent loosening path planning and dynamic fertilization regulation. SUMMARY

[0005] This section aims to summarize some aspects of the embodiments of the present application and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the specification to avoid obscuring the purpose of this section, abstract and title, and such simplifications or omissions cannot be used to limit the scope of the present application.

[0006] In view of the problems existing in the prior art, the present application is proposed.

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

[0008] A remote automatic management method for lotus root field cultivation, comprising:

[0009] Step S1: obtaining reflection wave data of the silt layer through an underwater ultrasonic array, the reflection wave data containing time domain characteristics and frequency domain characteristics;

[0010] Step S2: constructing a three-dimensional density field according to the echo delay time in the time domain characteristics and the main frequency decay rate in the frequency domain characteristics, the three-dimensional density field containing a density gradient value;

[0011] Step S3: when the density gradient value exceeds a preset threshold and the duration reaches a critical value, determining a compaction risk area and generating loosening path planning data.

[0012] Step S4: Send the soil loosening path planning data to the unmanned surface vessel to trigger the soil loosening drone to perform soil loosening operations according to the planned path;

[0013] Step S5: Simultaneously acquire pH and redox potential data of the caking risk zone, and calculate the amount of organic matter injected based on the density gradient value;

[0014] Step S6: Control the opening of the solenoid valve of the pre-buried pipe according to the amount of organic matter injected, so that the organic matter diffuses along the pore distribution channel after the soil is loosened.

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

[0016] S201: The temporal characteristics are obtained by convolving the echo peak intensity with a preset absorption coefficient matrix to obtain a preliminary density distribution;

[0017] S202: The frequency domain features are used to generate a density correction factor through a matching algorithm based on the spectral centroid offset;

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

[0019] In a preferred embodiment of the remote automated management method for lotus root field cultivation described in this invention, the formula for calculating the correction factor using the matching algorithm is as follows:

[0020]

[0021] in, These are empirical parameters. For standard reference frequency, This represents the spectral centroid offset.

[0022] As a preferred embodiment of the remote automated management method for lotus root field cultivation according to the present invention, the conditions for determining the risk zone of compaction in step S3 include:

[0023] A primary warning is activated when the density gradient value exceeds the first threshold.

[0024] When the density gradient value persists for a set time and the rate of change of density gradient between adjacent grids is greater than a set threshold percentage, it is marked as a high-risk area.

[0025] When generating the soil loosening path planning data, priority is given to processing aggregated areas that contain multiple consecutive high-risk areas.

[0026] As a preferred scheme of the remote automatic management method for lotus rhizome field cultivation, in step S5, the method for calculating the organic matter injection amount comprises:

[0027] The compensation equation is established as follows: , wherein:

[0028] is the organic matter injection amount, is the equipment coefficient;

[0029] is the density gradient value, is the pH sensitive coefficient;

[0030] is the ORP reference value, is the real-time ORP value;

[0031] When < the second threshold value, automatically switch to the emergency compensation mode, and multiply the value of by the amplification coefficient to amplify.

[0032] As a preferred scheme of the remote automatic management method for lotus rhizome field cultivation, in step S6, while controlling the opening degree of the electromagnetic valve:

[0033] According to the porosity distribution data after soil loosening, the distance D between adjacent injection ports is dynamically adjusted, and the calculation formula is:

[0034] , wherein:

[0035] is the initial distance;

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

[0037] is the maximum allowed density gradient difference value;

[0038] When , trigger the secondary soil loosening operation and recalculate the value of D.

[0039] As a preferred scheme of the remote automatic management method for lotus rhizome field cultivation, in step S6, while controlling the opening degree of the electromagnetic valve:

[0040] When the mutation rate of ultrasonic reflection wave data exceeds the safety threshold, the optical verification module is started; the silt layer image is obtained through the underwater camera, and the biological disturbance feature is identified by using the convolutional neural network; if the frog or snail activity feature is identified, the current density field data update is frozen until the disturbance feature disappears.

[0041] The remote automated management system applied to the remote automated management method for the lotus root field cultivation comprises:

[0042] An underwater ultrasonic detection module acquires reflection wave data of the silt layer through an underwater ultrasonic array, including time domain characteristics and frequency domain characteristics;

[0043] A density field construction and analysis module constructs a three-dimensional density field according to the ultrasonic data, analyzes a density gradient value, and identifies a hardening risk area;

[0044] A hardening risk assessment and path planning module detects the density gradient value, combines a continuous time length, determines the hardening risk area, and generates a soil loosening operation path;

[0045] An unmanned soil loosening operation module receives the soil loosening path data, controls a water surface unmanned ship and a soil loosening unmanned aerial vehicle to execute the soil loosening task according to the planned path;

[0046] An environmental parameter monitoring module is used for monitoring soil pH value and oxidation-reduction potential environmental data;

[0047] And an organic matter injection management module is used for calculating an organic matter injection amount according to the environmental data and the density gradient value, and dynamically adjusting the interval of adjacent injection ports according to the porosity distribution data after soil loosening, and controlling an organic matter diffusion channel.

[0048] The application further discloses a computer device comprising a memory and a processor, the memory stores a computer program, and the processor realizes the steps of the remote automated management method for the lotus root field cultivation when executing the computer program.

[0049] The application further discloses a computer readable storage medium, which stores a computer program, and the computer program realizes the steps of the remote automated management method for the lotus root field cultivation when being executed by a processor.

[0050] The application has the following beneficial effects:

[0051] 1、The application acquires reflection 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 a hardening area in real time, improves monitoring accuracy, generates soil loosening path planning data based on a density gradient value and oxidation-reduction potential data, and realizes accurate soil loosening by adopting a water surface unmanned ship and a soil loosening unmanned aerial vehicle for cooperative operation.

[0052] 2、The application combines an optical verification module and a convolutional neural network technology, can detect abnormalities in ultrasonic data, avoids the influence of biological disturbance on measurement accuracy, and improves system stability. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0054] Figure 1 The overall structure schematic diagram of a remote automatic management method for lotus root field cultivation is provided. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings.

[0056] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0057] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment.

[0058] REFERENCE Figure 1 For one embodiment of the present application, a remote automatic management method for lotus root field cultivation is provided, which comprises:

[0059] Step S1: The reflection wave data of the silt layer is obtained by the underwater ultrasonic array, the reflection wave data contains time domain characteristics and frequency domain characteristics, which provides basic data for subsequent density calculation and hardening risk judgment. Through the high-precision ultrasonic array, the resolution of the echo signal can be effectively improved, and the reliability of the data can be ensured.

[0060] Step S2: According to the echo delay time in the time domain characteristics and the main frequency attenuation rate in the frequency domain characteristics, a three-dimensional density field is constructed, which contains density gradient values.

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

[0062] S201: The time-domain feature is calculated by convolution with a preset absorption coefficient matrix to obtain a preliminary density distribution. The convolution calculation process is as follows:

[0063] Let be the change of the intensity of the echo signal with time, i.e., the amplitude signal of the ultrasonic echo at time t; let be a preset absorption coefficient matrix, then the convolution calculation formula used is:

[0064]

[0065] wherein the double summation operation in the above formula represents cumulative calculation in the entire time window (i dimension) and spatial region (j dimension), represents the preliminary calculated three-dimensional density distribution, and represents the density of the silt at the spatial position . The unit of the convolution calculation result is normalized according to the specific physical quantity to have actual physical meaning.

[0066] S202: The frequency-domain feature is used to generate a density correction factor by a matching algorithm through the spectral barycenter shift.

[0067] Specifically:

[0068] Let be the spectral barycenter shift (i.e., the degree of change from the reference spectrum), then the calculation formula for calculating the correction factor by the matching algorithm is:

[0069]

[0070] wherein is an empirical parameter, is a standard reference frequency.

[0071] S203: The preliminary density distribution is multiplied by the density correction factor to obtain the final three-dimensional density field data . The final density gradient value is a vector quantity, representing the rate of change of the density with the spatial coordinates, which is usually calculated using a gradient operator ( ):

[0072] ;

[0073] wherein represents the rate of change of the density in the x direction;

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

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

[0076] Step S3: When the density gradient value exceeds the preset threshold and the duration reaches the critical value, it is determined to be a soil compaction risk zone, and loosening path planning data is generated.

[0077] Specifically: A primary warning is triggered when the density gradient value exceeds the first threshold; for example, when the density gradient value G exceeds 0.15 g / cm³. 3 When the density gradient reaches / m, a primary warning is initiated, and the spatial coordinates of the area are recorded. At this time, soil loosening operations will not be triggered immediately, but a continuous monitoring state will be entered to observe the changing trend of the density gradient value.

[0078] If the density gradient value remains above 0.15 g / cm³ for a set period of time (e.g., 6 hours), 3 / m) and the rate of change of density gradient between adjacent grids ( When the threshold percentage is set (e.g., 5%), it is marked as a high-risk area, indicating that there may be severe compaction in the area, which requires special attention.

[0079] When generating soil loosening path planning data, priority is given to processing aggregated areas containing more than three consecutive high-risk zones to ensure the integrity and efficiency of soil loosening operations.

[0080] It is important to explain that the soil loosening path planning data is generated based on the existing A* algorithm grid search method. Simply put, it means: setting the starting point as the nearest high-risk grid and searching for the shortest path to adjacent grids; constraining the search direction to prioritize forward search to avoid drones hovering in low-risk areas; and avoiding path self-intersection to ensure smooth and efficient path planning.

[0081] Step S4: Send the soil loosening path planning data to the unmanned surface vessel to trigger the soil loosening drone to perform soil loosening operations according to the planned path.

[0082] Step S5: Simultaneously acquire pH and redox potential data of the slagging risk zone, and calculate the amount of organic matter injected by combining the density gradient value.

[0083] Specifically, the method for calculating the amount of organic matter injected in step S5 includes:

[0084] Establish the compensation equation: ,in:

[0085] The amount of organic matter injected. For equipment coefficients;

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

[0087] the ORP reference value, the real-time ORP value;

[0088] As can be seen from the above formula: the greater the soil density gradient (G increases), the more serious the degree of hardening, and more organic matter is needed to improve soil structure; pH affects the solubility of organic matter and the decomposition efficiency of microorganisms, so S is introduced to compensate.

[0089] At the same time, ORP reflects the oxidation-reduction capacity of the soil, as the denominator, to ensure that the injection amount increases under low oxidation state (i.e. low), to speed up soil recovery.

[0090] When < the second threshold value, this indicates that the soil is in an extremely low oxidation state, and automatically switches to an emergency compensation mode, increasing the value to 1.2 times the reference value to speed up the diffusion of organic matter and improve soil permeability.

[0091] Step S6. Control the opening degree of the electromagnetic valve of the embedded pipeline according to the organic matter injection amount, so that the organic matter diffuses along the pore distribution channel after soil loosening, and control the opening degree of the electromagnetic valve at the same time:

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

[0093] According to the porosity distribution data after soil loosening, the spacing D of adjacent injection ports is dynamically adjusted, and the calculation formula is:

[0094] , wherein:

[0095] is the initial spacing;

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

[0097] is the maximum allowed density gradient difference value;

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

[0099] In order to further optimize the distribution of organic matter, when , trigger the secondary tillage operation and recalculate the D value. Ensure the continuity of soil density adjustment, prevent uneven distribution of organic matter caused by poor growth. The threshold is set according to experimental data, typically set to:

[0100] = 0.2 g / cm 3 / m (for high clay soil);

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

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

[0103] In addition, it should be noted that when abnormal data occurs in the S1 step, the abnormal data needs to be processed, and the processing method includes:

[0104] When the mutation rate of ultrasonic reflection wave data exceeds the safety threshold, the optical verification module is started, which is to prevent data misjudgment caused by short-term environmental factors or abnormal interference, such as: in heavy rain or strong wind weather, the water surface and underwater environment may change dramatically, which may cause the mutation of ultrasonic data, but not the actual change of silt layer density. Therefore, cross-validation is performed using the optical verification module to improve the reliability of the data.

[0105] The silt layer image is obtained by the underwater camera, and the convolutional neural network is used to identify the biological disturbance feature; if the frog or snail activity feature is identified, the current density field data update is frozen until the disturbance feature disappears. The purpose is to use deep learning method to process underwater camera data and extract key features such as dynamic changes of particle distribution and abnormal wave patterns. Specifically, the convolutional neural network (CNN) is used to extract features from the obtained image, 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 is classified through the full connection layer. This method can effectively detect the micro-disturbance caused by frog activity and the crawling trace of snail in the silt layer.

[0106] In order to further fit the application scenario, for example: assuming that in a certain paddy field area, the mutation rate of ultrasonic reflection wave data is , and the safety threshold is set to If this occurs, the optical verification module is triggered. The underwater camera captures numerous small ripples, and the CNN identifies an 85% probability that these are frog activity. Therefore, the system freezes the density field data update for 30 minutes, and automatically resumes data acquisition after the frogs leave.

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

[0108] The underwater ultrasonic detection module acquires reflected wave data of the silt layer through an underwater ultrasonic array, including time-domain and frequency-domain characteristics; the density field construction and analysis module constructs a three-dimensional density field based on the ultrasonic data, analyzes the density gradient value, and identifies areas at risk of compaction; the compaction risk assessment and path planning module detects the density gradient value, combines it with the duration, determines the compaction risk area, and generates a soil loosening operation path.

[0109] The unmanned soil loosening operation module receives soil loosening path data and controls unmanned surface vessels and soil loosening drones to perform soil loosening tasks according to the planned path; the environmental parameter monitoring module is used to monitor soil pH value and redox potential environmental data; and the organic matter injection management module is used to calculate the amount of organic matter injected based on environmental data and density gradient values, and dynamically adjust the spacing between adjacent injection ports based on the porosity distribution data after soil loosening to control the organic matter diffusion channel.

[0110] This embodiment also provides a computer device applicable to a remote automated management method for lotus root field cultivation, comprising: 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 realize the remote automated management method for lotus root field cultivation as proposed in the above embodiment.

[0111] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0112] The embodiment also provides a storage medium, which stores a computer program, and the computer program is executed by a processor to implement the remote automatic management method for lotus rhizome field cultivation according to the above embodiment. The storage medium can be implemented by any type of volatile or nonvolatile storage devices or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk.

[0113] It should be noted that the above embodiment is only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A remote automated management method for field cultivation of lotus roots, characterized by, include: Step S1: Obtain reflected wave data of the silt layer using an underwater ultrasonic array. The reflected wave data includes time-domain and frequency-domain features. Step S2: Based on the echo delay time in the time domain features and the main frequency attenuation rate in the frequency domain features, construct a three-dimensional density field, which includes density gradient values; Step S3: When the density gradient value exceeds the preset threshold and the duration reaches the critical value, it is determined to be a soil compaction risk zone, and loosening path planning data is generated. Step S4: Send the soil loosening path planning data to the unmanned surface vessel to trigger the soil loosening drone to perform soil loosening operations according to the planned path; Step S5: Simultaneously acquire pH and redox potential data of the caking risk zone, and calculate the amount of organic matter injected based on the density gradient value; Step S6: Control the opening of the solenoid valve of the pre-buried pipe according to the amount of organic matter injected, so that the organic matter diffuses along the pore distribution channel after the soil is loosened.

2. A remote automated 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 temporal characteristics are obtained by convolving the echo peak intensity with a preset absorption coefficient matrix to obtain a preliminary density distribution; S202: The frequency domain feature spectrum centroid offset is used to generate a density correction factor through a matching algorithm; S203: Multiply the preliminary density distribution by the density correction factor to obtain the final three-dimensional density field data.

3. A remote automated management method for field cultivation of lotus roots according to claim 2, characterized in that: The formula for calculating the density correction factor generated by the matching algorithm is as follows: wherein, is an empirical parameter, is a standard reference frequency, is a spectral center of gravity offset.

4. A remote automated management method for field cultivation of lotus roots according to claim 3, characterized in that: The conditions for determining the risk zone of caking in step S3 include: A primary warning is activated when the density gradient value exceeds the first threshold. When the density gradient value persists for a set time and the rate of change of density gradient between adjacent grids is greater than a set threshold percentage, it is marked as a high-risk area. When generating the soil loosening path planning data, priority is given to processing aggregated areas that contain multiple consecutive high-risk areas.

5. A remote automated management method for field cultivation of lotus roots according to claim 4, characterized in that: The method for calculating the amount of organic matter injected in step S5 includes: The compensation equation is established: wherein: Organic matter injection rate, Equipment factor; is a density gradient value, is a pH sensitivity coefficient; is an ORP reference value, is a real-time ORP value; When When the second threshold is reached, the system switches automatically to the emergency compensation mode, which The value is multiplied by the amplification factor to amplify.

6. A remote automated management method for lotus rhizome field cultivation according to claim 5, characterized by: When controlling the opening degree of the solenoid valve in step S6: Based on the porosity distribution data after soil loosening, the spacing D between adjacent injection ports is dynamically adjusted. The calculation formula is as follows: wherein: is the initial separation; is the difference between the current density gradient value and the target value; to allow for the maximum density gradient difference; wherein, when a secondary tillage operation is triggered and the value of D is recalculated.

7. A remote automated management method for field cultivation of lotus roots according to claim 1, characterized in that: The method also includes anomaly data processing steps: When the mutation rate of ultrasonic reflected wave data exceeds the safety threshold, the optical verification module is activated; images of the silt layer are acquired through an underwater camera, and a convolutional neural network is used to identify biological disturbance characteristics; if frog or snail activity characteristics are identified, the current density field data update is frozen until the disturbance characteristics disappear.

8. A remote automated management system for lotus rhizome field cultivation, applied to the remote automated management method for lotus rhizome field cultivation according to any one of claims 1-7, characterized in that: The system includes: The underwater ultrasonic detection module acquires reflected wave data from the silt layer through an underwater ultrasonic array, including time-domain and frequency-domain characteristics. The density field construction and analysis module constructs a three-dimensional density field based on ultrasonic data, analyzes density gradient values, and identifies areas at risk of caking. The compaction risk assessment and path planning module detects density gradient values, combines them with duration, determines compaction risk areas, and generates loosening operation paths. The unmanned soil loosening module receives soil loosening path data and controls unmanned surface vessels and soil loosening drones to perform soil loosening tasks according to the planned path. The environmental parameter monitoring module is used to monitor soil pH and redox potential environmental data. And an organic matter injection management module is used for calculating the organic matter injection amount according to the environmental data and the density gradient value, and dynamically adjusting the interval of adjacent injection ports according to the post-tillage porosity distribution data, and controlling the organic matter diffusion channel. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The computer program is executed by the processor to realize the steps of the remote automatic management method of the lotus root field cultivation according to any one of claims 1-7.

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

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