Cooperative scheduling control method for weeding machines in field environment

Abstract

The invention relates to the field of automatic control, in particular to a cooperative scheduling control method of a weeding machine in a field environment, which comprises the following steps of: acquiring field and weeding related information by using a camera carried by an unmanned aerial vehicle, a multispectral sensor and different sensor equipment, preprocessing the data, and establishing a weeding operation priority evaluation neural network model; outputting a weeding operation priority evaluation result; obtaining a field weeding operation evaluation information confidence coefficient according to the output weeding operation priority evaluation information, setting a weeding operation evaluation information confidence coefficient threshold value, and calculating a real-time weeding operation speed by combining the field weeding operation evaluation information confidence coefficient with electric energy consumption; and calculating a field weeding efficiency measurement index through the obtained field weeding operation speed and weeding equipment performance, and if the obtained field weeding efficiency measurement index exceeds a weeding reference efficiency threshold range, starting preparation work countdown.

Description

Technical Field

[0001] The invention relates to the field of automatic control, and in particular to a coordinated scheduling control method for a weeder in a field environment. Background Art

[0002] In modern agricultural production, the field planting area is vast, and the growth of weeds will compete with crops for nutrients, water and sunlight, seriously affecting the yield and quality of crops. Weeding is an important part of field management. Traditional manual weeding methods are inefficient, labor-intensive and costly; and some existing weeders are mostly single-machine operations. When faced with large-scale field weeding tasks, there are problems such as long operation time and unreasonable resource utilization.

[0003] With the development of intelligent agriculture, the coordinated operation of multiple weeders has become an effective way to improve weeding efficiency and quality. However, there are still many deficiencies in the coordinated dispatching and control of multiple weeders in the field environment: it is difficult to reasonably allocate the priority operation area of ​​the weeder according to the complex conditions such as the terrain and weed distribution of the field; and each weeder cannot perform efficient weeding operations according to the actual power consumption, and cannot dynamically adjust the weeding operation speed, resulting in poor collaborative operation effects and low work efficiency; there is no weeding operation measurement indicator set to monitor the work, which is easy to cause resource waste and loss of control.

[0004] Therefore, a coordinated scheduling control method for weeders in a field environment is needed. Summary of the invention

[0005] The present invention provides a collaborative scheduling control method for a weeder in a field environment, the purpose of which is to reasonably allocate the priority operation areas of the weeder according to complex conditions such as the field topography and weed distribution, so as to achieve accurate collaborative operation and improve the coverage and efficiency of the weeding operation; in combination with the actual situation and the power consumption, the weeder can dynamically adjust the weeding operation speed to improve the operation efficiency, reduce energy consumption, and optimize the collaborative operation effect; establish weeding operation measurement indicators, monitor and evaluate the quality and efficiency of the weeding operation, and reduce the waste of time and resources.

[0006] The technical solution of the present invention is specifically as follows: A coordinated dispatching control method for a weeder in a field environment comprises the following steps: S1. Use drones equipped with cameras, multispectral sensors and different sensor devices to collect field and weeding related information and pre-process the data, establish a neural network model for weeding operation priority evaluation, and output the weeding operation priority evaluation results; S2. Obtain the confidence of the field weeding operation assessment information according to the output weeding operation priority assessment information, set the credibility threshold of the weeding operation assessment information at the same time, and calculate the real-time weeding operation speed by combining the confidence of the field weeding operation assessment information with the power consumption; S3. Calculate the field weeding efficiency measurement index by obtaining the field weeding operation speed and the performance of the weeding equipment. If the field weeding efficiency measurement index exceeds the weeding benchmark efficiency threshold range, start the countdown for the preparatory work.

[0007] Further, step S1 specifically includes: The neural network model for weeding operation priority evaluation includes input layer, convolution layer, dynamic analysis layer, fully connected layer, comprehensive evaluation layer, and output layer. The input of the neural network model for weeding operation priority evaluation is ,in, Represents the preprocessed field-related image information, It represents other relevant data information of the field after preprocessing; the convolution layer performs convolution operation on the preprocessed field-related image information; the image features extracted by the convolution layer are passed to the dynamic analysis layer; in the comprehensive evaluation layer, the priority of the field weeding blocks is comprehensively evaluated by combining the results of the dynamic analysis layer and other relevant data information of the field after preprocessing; then it enters the fully connected layer, and the fully connected layer outputs the evaluation results of the field conditions, and finally the output layer outputs the weeding priority evaluation results as a whole.

[0008] Further, in the dynamic analysis layer, the specific process is as follows: in, Represents the output of the dynamic analysis layer, which is represented in The analysis results of field images during different time periods; A feature vector representing a field-related image; Indicates the state of the field image in the period before the recording starts; Indicates The sensitivity of a feature; Indicates Standard reference value for each characteristic; Indicates the weight value at different times under the influence of climate; Represents projection transformation parameters; express At the The element value at the location of the regional vegetation index index; express The period is The gradient amplitude at the location segmentation of the regional vegetation index; represents the error coefficient; Indicates dynamic positioning parameters.

[0009] Further, step S2 specifically includes: Define the first credibility threshold comparison parameter of weeding operation evaluation information , the second credibility threshold comparison parameter ,in, , the confidence level of the weeding operation assessment information Compare the parameters with the first credibility threshold respectively Compare the parameter with the second credibility threshold for comparison.

[0010] Furthermore, when the confidence of the weeding operation evaluation information is , then the weeding operation speed correction parameter is , ; When the confidence level of the weed control operation assessment information is , then the weeding operation speed correction parameter is , ； When the confidence level of weed control assessment information is obtained , then the weeding operation speed correction parameter is ， ; in, Indicates the standard weeding operation speed; , They respectively represent the influence coefficient of built-in confidence on the weeding operation speed in the corresponding threshold interval.

[0011] Further, step S2 specifically includes: , Represents a function that represents the confidence of the weeding operation evaluation information and weeding speed The mapping relationship between them; define the power consumption as , calculate the real-time weeding operation speed , Represents the power consumption control coefficient. The specific process is as follows: in, represents the first boundary; Indicates the second boundary.

[0012] Further, step S3 specifically includes: Calculation of field weed control efficiency test index , and a threshold range of weed control efficiency is set. If the field weed control efficiency determination index is If the weeding benchmark efficiency threshold is exceeded, the work monitoring module is entered and the preparatory work countdown is started.

[0013] Further, the calculation process of the field weed control efficiency determination index is as follows:

[0014] in, The weight coefficient representing the speed of field weeding operation; A weight coefficient indicating the performance of the weeding equipment; Indicates the speed parameter of field weeding operation; Indicates the performance parameters of weeding equipment.

[0015] Further, step S3 specifically includes: defining the comparison parameters of the field weed control efficiency determination index ; when ≥ When the field weed control efficiency is determined, the field weed control efficiency index is obtained. When the threshold of the weed control efficiency is exceeded, the countdown for the preparation work is started; The countdown for preparation will not start.

[0016] Beneficial effects: 1. By establishing a neural network model for evaluating the priority of weeding operations, the control system can perform priority evaluation on weeding operations based on the collected data, which helps to determine which areas need to be given priority for weeding operations, improve operation efficiency and effects, realize intelligent scheduling, and maximize weeding efficiency; precise monitoring is performed based on the evaluation results of operation priorities, which effectively reduces duplication of labor and waste of resources in agricultural operations, helps field managers reduce costs, improve production efficiency, and provide data support for subsequent control processes.

[0017] 2. The present invention adjusts the weeding operation speed according to the situation by considering the confidence of the weeding operation evaluation information and the power consumption, thereby improving the stability of the control system; dynamically adjusting the weeding operation speed can optimize the system performance, ensure that more accurate weeding operation evaluation results can be obtained under different power consumption and evaluation conditions, and can effectively save the use of control system resources and avoid high-speed weeding operations and processing when unnecessary. At the same time, by dynamically controlling the working state of the equipment, it only enters the preparation and working state when the weeding machine information acquisition task is required, which can effectively save energy; when there is no task, the equipment can enter the low-power initial start state to reduce unnecessary energy consumption; it can also improve the response speed of the system to ensure that the weeding machine information acquisition can be carried out in time when needed.

[0018] 3. The present invention dynamically controls the field weeding control work according to the field weeding efficiency measurement index, discovers problems in time and takes measures to ensure that the weeding operation is always in a high-efficiency state; by real-time monitoring and adjusting the weeding operation speed and equipment performance, the efficiency of field weeding can be maximized and the waste of time and resources can be reduced; at the same time, the weeding operation is scheduled and controlled according to the actual situation, and the weeding equipment and human resources are used more effectively, thereby reducing costs and increasing output; through the automated scheduling and control method, human errors and waste are reduced, ensuring that the field weeding operation is carried out in the best way, and improving the quality and efficiency of the operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of a coordinated scheduling control method of a weeder in a field environment of the present invention; Figure 2 It is a module diagram of a coordinated dispatching control system of a weeder in a field environment of the present invention; Figure 3 It is a schematic diagram of the intelligent weed control processing unit of the present invention. DETAILED DESCRIPTION

[0020] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should also be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] Refer to the attached Figure 1 This embodiment provides a coordinated scheduling control method for a weeder in a field environment, comprising the following steps: S1. Use drones equipped with high-definition cameras, multispectral sensors, and various related sensors and other monitoring equipment to collect field and weeding related information and pre-process the data, establish a neural network model for weeding operation priority evaluation, and output the weeding operation priority evaluation results.

[0022] Use drones equipped with high-definition cameras and multispectral sensors to conduct all-round scanning of fields to obtain topographic information, crop distribution information, and weed distribution information. Among them, high-definition cameras are used to capture visible light images of fields, and the distribution locations of crops and weeds are identified through image analysis. Multispectral sensors are used to collect spectral data in different bands, and the distribution range and density of weeds are more accurately determined based on the differences in the reflective characteristics of crops and weeds in different spectral bands. At the same time, various related sensors are used to obtain meteorological data, soil data, weeder working data, vegetation index data, crop growth data, etc., to provide data support for subsequent modeling and analysis, thereby improving weeding production efficiency.

[0023] At the same time, the collected images and spectral data are transmitted to the ground control center, which uses geographic information system (GIS) technology to process and analyze the data to generate a three-dimensional terrain model of the field, crop distribution map, and weed density distribution map. Then, the acquired field-related information is preprocessed using existing technology to establish a neural network model for weeding operation priority assessment. The preprocessed field-related images and field soil, vegetation and other related information are input into the neural network model for weeding operation priority assessment, and the corresponding weeding operation priority assessment results are finally output through deep learning.

[0024] The neural network model for weeding operation priority evaluation includes input layer, convolution layer, dynamic analysis layer, fully connected layer, comprehensive evaluation layer, and output layer. The input of the neural network model for weeding operation priority evaluation is ,in, Represents the preprocessed field-related image information, Indicates other relevant data information of the field after preprocessing. , the convolution layer performs convolution operation on the preprocessed field-related image information to obtain the feature vector of the field-related image. The specific process is as follows: in, A feature vector representing a field-related image; represents the convolution function; Indicates the weight value; Represents the bias of the convolutional layer; the image features extracted by the convolutional layer are passed to the dynamic analysis layer, and the field situation is further dynamically analyzed in the dynamic analysis layer. The specific process is as follows: in, Represents the output of the dynamic analysis layer, which is represented in The analysis results of field images during different time periods; Indicates the state of the field image in the period before the recording starts; Indicates The sensitivity of a feature; Indicates Standard reference value for each characteristic; Indicates the weight value at different times under the influence of climate; Represents projection transformation parameters; express At the The element value at the location of the regional vegetation index index; express The period is The gradient amplitude at the location segmentation of the regional vegetation index; represents the error coefficient; Represents the dynamic positioning parameter. At the same time, dimensionality reduction is performed to reduce the complexity of the data, remove noise and redundant information, and the weeding operation priority evaluation system can process data more efficiently and improve the performance and generalization ability of the model.

[0025] In the comprehensive evaluation layer, the results of the dynamic analysis layer and other relevant data information of the pre-processed fields are combined Comprehensively evaluate the priority of field weeding blocks. The specific process is as follows: in, Represents the output result of the comprehensive evaluation layer; Indicates soil nutrient content; represents the proportionality coefficient, which indicates the effect of soil nutrient changes on changes in weed growth status; Indicates vegetation or crop cover; It represents the time correction factor, which is used to adjust the changes of other relevant data information of the field over time; represents the sensitivity coefficient; represents the magnification factor, which amplifies the impact of the analysis results of the field image on the comprehensive evaluation process; represents the weighting coefficient of sunlight on the comprehensive evaluation process; Indicates the intensity of sunlight.

[0026] Then it enters the fully connected layer and the Softmax function, and the fully connected layer outputs the evaluation results of the field conditions. Finally, the output layer outputs the overall weeding priority evaluation results. The output result is In the embodiment of the present invention, for the field blocks with priority weeding operations, , and its corresponding output is , after the Softmax function, the field blocks for weeding operations The confidence level can be expressed as: in, Indicates the number of field blocks that require weeding operations; Indicates traversal of variables.

[0027] The error is then calculated using the loss function, and the parameters in the weeding operation priority evaluation neural network are optimized through gradient descent to train an accurate weeding operation priority evaluation neural network model.

[0028] By establishing a neural network model for priority evaluation of weeding operations, the present invention can enable the control system to perform priority evaluation of weeding operations based on the collected data, which helps to determine which areas need to be given priority for weeding operations, improve operation efficiency and effects, realize intelligent scheduling, and maximize weeding efficiency; accurate monitoring is performed based on the evaluation results of operation priorities, effectively reducing duplication of labor and waste of resources in agricultural operations, helping field managers reduce costs, improve production efficiency, and provide data support for subsequent control processes.

[0029] S2. Obtain the confidence of the field weeding operation assessment information based on the output weeding operation priority assessment information , and set the credibility threshold of weeding operation assessment information , and the weeding operation speed is calculated from the license plate recognition information, that is, the license plate information confidence , where the weeding speed correction parameter is defined , Define the first credibility threshold comparison parameter of weeding operation evaluation information , the second credibility threshold comparison parameter ,in, , the confidence level of the weeding operation assessment information Compare the parameters with the first credibility threshold respectively Compare the parameter with the second credibility threshold Compare and obtain the weeding operation speed correction parameter based on the comparison results , , Indicates the standard weeding operation speed, the specific process is as follows: When the confidence level of the weed control operation assessment information is , then the weeding operation speed correction parameter is , ; When the confidence level of the weed control operation assessment information is , then the weeding operation speed correction parameter is , ； When the confidence level of weed control assessment information is obtained , then the weeding operation speed correction parameter is ， ; in, , They respectively represent the influence coefficient of built-in confidence on the weeding operation speed in the corresponding threshold interval.

[0030] in, , improve the value of weeding operation speed. The system believes that the current weeding operation evaluation information has a high confidence level, but there is still room for improvement. For example, when the weed coverage in the field is good but there may be some minor interference (such as environmental impact), increasing the weeding operation speed can increase the chance of obtaining more accurate weeding efficiency, thereby improving the overall evaluation effect; , the value of keeping the weeding operation speed unchanged indicates that the confidence of the weeding operation evaluation information is in an intermediate range. The system believes that the current weeding operation speed is appropriate and does not need to be adjusted. For example, when the field weed coverage basically meets the requirements but there is still room for improvement, maintaining the current weeding operation speed can continue to obtain the weed coverage for evaluation and avoid system fluctuations caused by unnecessary weeding speed adjustments; , reducing the value of the weeding operation speed means that the system believes that the confidence of the current weeding operation evaluation information is low, which may be due to abnormal vegetation index data or unstable meteorological information, resulting in reduced confidence. The system may reduce the speed of the weeding machine to reduce possible erroneous operations and ensure the accuracy of the operation.

[0031] At the same time, set the confidence level of the weeding operation assessment information and weeding speed The original relationship is: , Represents a function that represents the confidence of the weeding operation evaluation information and weeding speed The mapping relationship between them; define the power consumption as , calculate the real-time weeding operation speed , Represents the power consumption control coefficient. The specific process is as follows: in, represents the first boundary; represents the second boundary; The power consumption is within the normal range; Indicates high power consumption; Indicates lower power consumption.

[0032] In an embodiment of the present invention, the measuring device records the speed of field weeding operations and enters a countdown for preparatory work. If the monitoring device detects that there is a task to obtain information about a weeding machine, it is in a preparatory state, and the countdown for preparatory work starts to count down. If the countdown for preparatory work returns to zero, the work monitoring module enters a working state. If the monitoring device detects that there is no task to obtain information about a weeding machine, the countdown for preparatory work enters an initial start state. In an embodiment of the present invention, for example, the countdown for preparatory work is set to 8 seconds. When a task to obtain information about a weeding machine is detected, the countdown for preparatory work starts to count down, such as: 8,...,5,...,3,...,1..., when the value returns to zero, the work monitoring module enters a working state.

[0033] The present invention can adjust the weeding operation speed according to the situation by considering the confidence of the weeding operation evaluation information and the power consumption, thereby improving the stability of the control system; dynamically adjusting the weeding operation speed can optimize the system performance, ensure that more accurate weeding operation evaluation results can be obtained under different power consumption and evaluation conditions, and can effectively save the use of control system resources and avoid high-speed weeding operations and processing under unnecessary circumstances. At the same time, by dynamically controlling the working state of the equipment, only entering the preparation and working state when the weeding machine information acquisition task is required, it can effectively save energy; when there is no task, the equipment can enter the low-power initial start state to reduce unnecessary energy consumption; it can also improve the response speed of the system to ensure that the weeding machine information acquisition can be carried out in time when needed.

[0034] S3. Obtain the field weeding operation speed by Weeding equipment performance , calculate the field weed control efficiency determination index If we get the field weed control efficiency measurement index When the threshold range of the baseline weed control efficiency is exceeded, the countdown for preparatory work will be initiated.

[0035] The field weeding operation speed is obtained in the field weeding information collection unit Weeding equipment performance In the embodiment of the present invention, the field weeding operation speed refers to the speed of completing the weed removal work in the field within a unit time; the weeding equipment performance refers to the working efficiency and accuracy of the equipment or machine for weeding operation, which is measured by the maintenance frequency of the weeding equipment.

[0036] According to the acquired test data, calculate the field weed control efficiency measurement index , and the collaborative control module has a weed control benchmark efficiency threshold range. If the field weed control efficiency measurement index is If the weeding benchmark efficiency threshold is exceeded, the work monitoring module is entered and the preparatory work countdown is started. In the embodiment of the present invention, the calculation process of the field weeding efficiency determination index is as follows: in, The weight coefficient representing the speed of field weeding operation; A weight coefficient indicating the performance of the weeding equipment; Indicates the speed parameter of field weeding operation; Indicates the performance parameters of weeding equipment.

[0037] The specific judgment process is that the collaborative control module is provided with a field weeding efficiency determination index comparison parameter ; when ≥ When the field weed control efficiency is determined, the field weed control efficiency index is obtained. When the threshold range of the weed control benchmark efficiency is exceeded, the countdown for the preparation work is started and the work state is entered; when At this time, the preparatory work countdown is not started. At the same time, the weeding efficiency compensation module starts to improve the operation speed and equipment performance information through existing technologies known to those skilled in the art, such as: operation path planning optimization, maintenance frequency adaptation, etc.

[0038] The present invention dynamically controls the field weeding control work according to the field weeding efficiency measurement index, finds problems in time and takes measures to ensure that the weeding operation is always in a high-efficiency state; by real-time monitoring and adjusting the weeding operation speed and equipment performance, the efficiency of field weeding can be maximized and the waste of time and resources can be reduced; at the same time, the weeding operation is scheduled and controlled according to actual conditions, and the weeding equipment and human resources are used more effectively, thereby reducing costs and increasing output; through the automated scheduling control method, human errors and waste are reduced, ensuring that the field weeding operation is carried out in the best way, and improving the operation quality and efficiency.

[0039] Refer to the attached Figure 2 This embodiment provides a coordinated scheduling control system for a weeder in a field environment, including the following contents: Field weeding information collection unit, weeding operation priority assessment unit, intelligent weeding control processing unit; The field weeding information collection unit is used to collect various information in the field in real time, such as crop growth conditions, weed distribution, soil properties, etc., through various sensors, monitoring equipment, drones and other technologies; obtain data and transmit it to the system to provide support for subsequent analysis and decision-making.

[0040] A weeding operation priority assessment unit is used to establish a weeding operation priority assessment neural network model through preprocessing, data analysis and other technologies to assess the weeding needs of different areas in the field; Refer to the attached Figure 3 , an intelligent weeding control processing unit, which is used to realize intelligent weeding robot scheduling and control according to the priority evaluation results of weeding operations; Among them, the intelligent weeding control processing unit includes a weeding operation speed acquisition module, an energy consumption module, a work monitoring module, a weeding efficiency compensation module, and a collaborative control module; The weeding operation speed acquisition module is responsible for real-time monitoring and acquisition of weeder operation speed data, adjusting the speed according to the operation conditions in different areas of the field to ensure efficient weeding operations; The power consumption module is used to monitor and evaluate the power consumption of the weeding robot and to manage and optimize the power; The work monitoring module is used to monitor the working status and efficiency of the weeder, and to enter the working state in time or find problems and take corresponding measures to ensure that the weeding operation proceeds as planned; The weeding efficiency compensation module is used to make compensation adjustments based on the actual weeding efficiency and make corresponding adjustments based on the feedback of field weeding efficiency measurement indicators to improve weeding efficiency and ensure operation quality; The collaborative control module is used to work in collaboration with other system modules to achieve intelligent control and scheduling of the overall weeding operation.

[0041] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0042] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0043] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0044] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0045] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A coordinated dispatching control method for a weeder in a field environment, characterized in that: The following steps are involved: S1. Use drones equipped with cameras, multispectral sensors and different sensor devices to collect field and weeding related information and pre-process the data, establish a neural network model for weeding operation priority evaluation, and output the weeding operation priority evaluation results; S2. Obtain the confidence of the field weeding operation assessment information according to the output weeding operation priority assessment information, set the credibility threshold of the weeding operation assessment information at the same time, and calculate the real-time weeding operation speed by combining the confidence of the field weeding operation assessment information with the power consumption; S3. Calculate the field weeding efficiency measurement index by obtaining the field weeding operation speed and the performance of the weeding equipment. If the field weeding efficiency measurement index exceeds the weeding benchmark efficiency threshold range, start the countdown for the preparatory work.

2. The coordinated dispatching control method of a weeder in a field environment according to claim 1 is characterized in that: The step S1 specifically includes: The neural network model for weeding operation priority evaluation includes input layer, convolution layer, dynamic analysis layer, fully connected layer, comprehensive evaluation layer, and output layer. The input of the neural network model for weeding operation priority evaluation is ,in, Represents the preprocessed field-related image information, It represents other relevant data information of the field after preprocessing; the convolution layer performs convolution operation on the preprocessed field-related image information; the image features extracted by the convolution layer are passed to the dynamic analysis layer; in the comprehensive evaluation layer, the priority of the field weeding blocks is comprehensively evaluated by combining the results of the dynamic analysis layer and other relevant data information of the field after preprocessing; then it enters the fully connected layer, and the fully connected layer outputs the evaluation results of the field conditions, and finally the output layer outputs the weeding priority evaluation results as a whole.

3. The coordinated dispatching control method of a weeder in a field environment according to claim 2 is characterized in that: In the dynamic analysis layer, the specific process is as follows: in, Represents the output of the dynamic analysis layer, which is represented in The analysis results of field images during different time periods; A feature vector representing a field-related image; Indicates the state of the field image in the period before the recording starts; Indicates The sensitivity of a feature; Indicates Standard reference value for each characteristic; Indicates the weight value at different times under the influence of climate; Represents projection transformation parameters; express At the The element value at the location of the regional vegetation index index; express The period is The gradient amplitude at the location segmentation of the regional vegetation index; represents the error coefficient; Indicates dynamic positioning parameters.

4. The coordinated dispatching control method of a weeder in a field environment according to claim 1, characterized in that: The step S2 specifically includes: Define the first credibility threshold comparison parameter of weeding operation evaluation information , the second credibility threshold comparison parameter ,in, , the confidence level of the weeding operation assessment information Compare the parameters with the first credibility threshold respectively Compare the parameter with the second credibility threshold for comparison.

5. The coordinated dispatching control method of a weeder in a field environment according to claim 4 is characterized in that: The step S2 specifically includes: When the confidence level of the weed control operation assessment information is , then the weeding operation speed correction parameter is , ; When the confidence level of the weed control operation assessment information is , then the weeding operation speed correction parameter is , ； When the confidence level of weed control assessment information is obtained , then the weeding operation speed correction parameter is ， ; in, Indicates the standard weeding operation speed; , They respectively represent the influence coefficient of built-in confidence on the weeding operation speed in the corresponding threshold interval.

6. The coordinated dispatching control method of a weeder in a field environment according to claim 5, characterized in that: The step S2 specifically includes: , Represents a function that represents the confidence of the weeding operation evaluation information and weeding speed The mapping relationship between them; define the power consumption as , calculate the real-time weeding operation speed , Represents the power consumption control coefficient. The specific process is as follows: Among them, among them, represents the first boundary; Indicates the second boundary.

7. The coordinated dispatching control method of a weeder in a field environment according to claim 1, characterized in that: The step S3 specifically includes: Calculation of field weed control efficiency test index , and a threshold range of weed control efficiency is set. If the field weed control efficiency determination index is If the weeding benchmark efficiency threshold is exceeded, the work monitoring module is entered and the preparatory work countdown is started.

8. The coordinated dispatching control method of a weeder in a field environment according to claim 7, characterized in that: The calculation process of the field weed control efficiency determination index is as follows: in, The weight coefficient representing the speed of field weeding operation; A weight coefficient indicating the performance of the weeding equipment; Indicates the speed parameter of field weeding operation; Indicates the performance parameters of weeding equipment.

9. The coordinated dispatching control method of a weeder in a field environment according to claim 7, characterized in that: The step S3 specifically includes: defining the comparison parameters of the field weed control efficiency determination index ; when ≥ When the field weed control efficiency is determined, the field weed control efficiency index is obtained. When the threshold of the weed control efficiency is exceeded, the countdown for the preparation work is started; The countdown for preparation will not start.

Images