Method, system and equipment for monitoring quality of trenching operation based on rapeseed planting

Abstract

The present invention provides a method, system and equipment for monitoring the quality of trenching operations based on rapeseed planting, and relates to the technical field of agricultural engineering. The present invention cleans and standardizes the collected data, calculates the characteristic value deviation and generates a characteristic vector, and then establishes a quality evaluation model through a neural network convolution structure to output a trench quality index. The evaluation index is calculated in combination with the temperature and humidity data of the actual environment, and the threshold is dynamically corrected to form a correction value. By comparing the trench quality index and the correction value, it is determined whether the trench meets the trenching quality standard. The present invention realizes dynamic matching and optimization of the trench quality standard and the rapeseed planting demand by collecting and processing the trench characteristic data after the trenching machine operation and combining the relevant parameters of the trench after trenching.

Description

Technical Field

[0001] The present invention relates to the field of agricultural engineering technology, and in particular to a method, system and equipment for monitoring the quality of trenching operations based on rapeseed planting. Background Art

[0002] Ditching plays a vital role in rapeseed cultivation, especially in the tillage and sowing stages. Reasonable ditching depth, width and ditch bottom flatness directly affect the growth environment, germination rate and later irrigation effect of rapeseed. Therefore, monitoring the quality of ditching becomes particularly important. Ditching of rapeseed is an important part of agricultural production. Especially in the context of the popularization of mechanized tillage, how to improve the quality and efficiency of ditching has become one of the key issues in the development of modern agriculture. Especially in the process of mechanized operation, manual control is difficult and the quality fluctuates greatly, so it is necessary to rely on intelligent technology for quality monitoring and optimization. The quality of ditching is directly related to the looseness of the soil, sowing depth, drainage in the ditch and the later irrigation effect. Therefore, monitoring the quality of ditching has become an important part of the intelligent agricultural management system. However, the traditional ditching quality monitoring method mainly relies on manual measurement and visual evaluation, supplemented by simple measurement tools and empirical analysis, and is judged by checking the length, width, slope and collapse or obvious defects of the ditch wall.

[0003] Insufficient existing technology:

[0004] Traditional ditching quality monitoring has problems such as low efficiency, insufficient accuracy, lack of real-time and dynamic environmental considerations. Traditional methods rely on manual visual inspection and simple tools, which are highly subjective and have low accuracy. They cannot comprehensively evaluate the linkage effects of multi-dimensional features such as width, depth and angle, and ignore the dynamic effects of environmental factors such as temperature, humidity and soil permeability on ditch quality.

[0005] In large-scale rapeseed planting or other agricultural projects, the quality monitoring of a large number of ditches will lag behind due to the low efficiency and poor accuracy of traditional methods, affecting the overall agricultural planning and production efficiency. In addition, traditional evaluation methods are not flexible enough and difficult to adapt to different operating environments and ditch types.

[0006] Therefore, it is necessary to provide a method, system and equipment for monitoring the quality of trenching operations based on rapeseed planting to solve the above problems.

[0007] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0008] The purpose of the present invention is to provide a method, system and equipment for monitoring the quality of trenching operations based on rapeseed planting, so as to solve the problems raised in the above-mentioned background technology.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A method for monitoring the quality of trenching operations based on rapeseed planting, the specific steps comprising:

[0011] Step 1: Collect relevant characteristic data of the ditch after the trenching machine operates, wherein the relevant characteristic data includes ditch width, ditch depth, ditch length and ditch opening angle;

[0012] Step 2: After preprocessing the relevant characteristic data of the ditch, the deviations of the ditch width, ditch depth and ditch opening angle from their corresponding standard values ​​are calculated respectively, and a characteristic vector of the ditch is generated according to the deviations between the characteristic data and the standard values. The preprocessing includes characteristic data cleaning and data standardization processing;

[0013] Step 3: Construct a quality evaluation model using a convolutional neural network structure, including an input layer, a hidden layer, and an output layer. The input layer receives feature vectors, the hidden layer is responsible for data processing of feature vectors, and the output layer is used to output the ditch quality index. The feature vectors are input into the quality evaluation model to obtain the ditch quality index.

[0014] Step 4: Collect the optimal temperature and humidity of the ditch after the ditching operation, the current temperature and humidity, and the soil permeability of the ditch, and calculate the quality evaluation index of the ditch by combining the temperature deviation value and the humidity deviation value;

[0015] Step 5: Use the generated ditch quality evaluation index to correct the evaluation threshold to obtain the evaluation threshold correction value, compare the quality index with the evaluation threshold correction value, and determine whether the ditch meets the ditching quality standard.

[0016] Furthermore, the relevant characteristic data of the ditch including the ditch width, ditch depth, ditch length and ditch opening angle are preprocessed, and the preprocessing includes characteristic data cleaning and data standardization, and the method is as follows:

[0017] Data cleaning of feature data includes detection and deletion of outliers and duplicate data, and processing of missing values. Specifically, it uses statistical methods to identify outliers and duplicate data in feature data, deletes outliers and duplicate data in feature data, and uses the mean, median or mode of feature data to fill in missing values ​​in feature data.

[0018] The feature data is normalized as minimum-maximum normalization, scaling the data to Within the range, all feature data have the same scale, based on the formula:

[0019] ;

[0020] in, is the feature data after normalization. is the original feature data, is the minimum value of the same type of feature data in the data set. It is the maximum value of the same type of feature data in the dataset.

[0021] Furthermore, the deviations of the ditch width, ditch depth, ditch length and ditch opening angle from their respective standard values ​​are calculated according to the following method:

[0022] The standard values ​​of ditch width, ditch depth and ditch opening angle after ditching are obtained from the historical ditching data, and the deviation values ​​of each parameter are calculated based on the following formula:

[0023] ;

[0024] ;

[0025] ;

[0026] ;

[0027] in, , , , Respectively represent the deviation values ​​of ditch width, depth, length and opening angle, , , , The collected ditch width, depth, length and opening angle data are respectively: , , , They are the standard values ​​of ditch width, depth, length and opening angle respectively.

[0028] Furthermore, the quality evaluation model is constructed based on the following method:

[0029] The calculated deviation values ​​of ditch width, depth, length and opening angle are used to comprehensively generate the ditch feature vector, and the combined ditch feature vector is used as the input of the model, and the ditch quality index is used as the label;

[0030] The quality evaluation model adopts a neural network convolution structure, including an input layer, a hidden layer and an output layer. The input layer is used to receive the feature vector of the ditch, the hidden layer is used to process the feature vector of the ditch, and the output layer is used to output the ditch quality index. ;

[0031] Select the mean square error function as the loss function, calculate the loss function value based on the output result and the true label, and calculate the gradient through the back propagation algorithm to update the weights and bias of the neural network. Repeat the above operations until the model reaches the predetermined number of training rounds.

[0032] Furthermore, the quality evaluation index of the ditch is calculated based on the following method:

[0033] Obtain the optimal temperature and humidity of the ditch after the ditching operation, and measure the current temperature and humidity by arranging the temperature and humidity sensors arranged on the ditch, and calculate the deviation between the current temperature, current humidity and the optimal temperature and humidity;

[0034] Calculation of ditch soil permeability using double ring infiltration method ;

[0035] The formula for calculating the quality evaluation index of the ditch is:

[0036] ;

[0037] in, It represents the quality evaluation index of the ditch. , is the current temperature and the current humidity, , To achieve the best temperature and humidity of the ditch after trenching. is the deviation between the current temperature and the optimal temperature, is the deviation between the current humidity and the optimal humidity, is the ditch soil permeability.

[0038] Furthermore, the evaluation threshold is corrected using the generated ditch quality evaluation index to obtain a corrected evaluation threshold value, and the quality index is compared with the corrected evaluation threshold value, based on the formula:

[0039] ;

[0040] in, represents the evaluation threshold correction value, is the evaluation threshold;

[0041] According to the obtained evaluation threshold correction value, the size of the ditch quality index and the evaluation threshold correction value is judged based on the following logic:

[0042] ;

[0043] in, The logical value indicating the size of the ditch quality index and the evaluation threshold correction value. When , it indicates that the ditch quality index is less than the evaluation threshold correction value and does not meet the ditching quality standard; when , indicating that the ditch quality index is not less than the evaluation threshold correction value, and meets the ditching quality standard. is the ditch quality index.

[0044] The present invention also provides a ditching operation quality monitoring system based on rapeseed planting, and the quality monitoring system is used to execute the above-mentioned ditching operation quality monitoring method based on rapeseed planting, comprising:

[0045] A feature data collection module, wherein the feature data collection module is used to collect relevant feature data of the ditch after the ditching machine operates, wherein the relevant feature data includes ditch width, ditch depth, ditch length and ditch opening angle;

[0046] A deviation value calculation module, wherein the deviation value calculation module is used to calculate the deviation values ​​of the ditch width, ditch depth and ditch opening angle from their respective corresponding standard values ​​after preprocessing the relevant characteristic data of the ditch, and to generate a characteristic vector of the ditch according to the deviation values ​​between the characteristic data and the standard value, wherein the preprocessing includes characteristic data cleaning and data standardization processing;

[0047] A quality evaluation model and index calculation module, which is used to construct a quality evaluation model, and adopts a neural network convolution structure, including an input layer, a hidden layer and an output layer, wherein the input layer receives a feature vector, the hidden layer is responsible for data processing of the feature vector, and the output layer is used to output a ditch quality index, and the feature vector is input into the quality evaluation model to obtain the ditch quality index;

[0048] A quality index generation module, which is used to collect the optimal temperature and humidity of the ditch after the ditching operation and the current temperature and humidity as well as the soil permeability of the ditch, and calculate the quality evaluation index of the ditch by combining the temperature deviation value and the humidity deviation value;

[0049] The correction judgment module is used to correct the evaluation threshold value by using the generated ditch quality evaluation index to obtain the evaluation threshold correction value, compare the quality index with the evaluation threshold correction value, and judge whether the ditch meets the ditching quality standard.

[0050] The present invention further provides an electronic device, comprising:

[0051] A memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic equipment to execute the above-mentioned method for monitoring the quality of trenching operations based on rapeseed planting.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] By combining sensor data collection, standardized cleaning processing and multi-dimensional data modeling, the present invention uses a neural network model to generate quantitative quality indicators, significantly improving the accuracy and objectivity of the data, and realizing rapid and scientific ditch quality evaluation through real-time monitoring and dynamic threshold correction;

[0054] The present invention also has significant advantages in systematic and large-scale agricultural applications. Through automated feature data collection, data preprocessing and model calculation, it can not only quickly evaluate the quality of large-scale ditching operations, but also dynamically adjust quality standards to adapt to different environmental requirements and meet the needs of efficient and accurate monitoring in modern agriculture. At the same time, combined with historical data analysis and predictive modeling capabilities, it provides scientific guidance for future ditch excavation results;

[0055] The present invention realizes real-time, accurate and dynamic quality monitoring and evaluation of trenching operations through sensor data collection, multi-dimensional modeling and neural network analysis, and comprehensively improves the efficiency and scientificity of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of the overall method flow of the present invention.

[0057] Figure 2 It is a schematic diagram of the system module flow of the present invention. DETAILED DESCRIPTION

[0058] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0059] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0060] Example:

[0061] See also Figure 1 , a method for monitoring the quality of trenching operations based on rapeseed planting, the specific steps include:

[0062] Step 1: Collect relevant characteristic data of the ditch after the trenching machine operates, wherein the relevant characteristic data includes ditch width, ditch depth, ditch length and ditch opening angle;

[0063] Step 2: After preprocessing the relevant characteristic data of the ditch, the deviations of the ditch width, ditch depth and ditch opening angle from their corresponding standard values ​​are calculated respectively, and a characteristic vector of the ditch is generated according to the deviations between the characteristic data and the standard values. The preprocessing includes characteristic data cleaning and data standardization processing;

[0064] Step 3: Construct a quality evaluation model using a convolutional neural network structure, including an input layer, a hidden layer, and an output layer. The input layer receives feature vectors, the hidden layer is responsible for data processing of feature vectors, and the output layer is used to output the ditch quality index. The feature vectors are input into the quality evaluation model to obtain the ditch quality index.

[0065] Step 4: Collect the optimal temperature and humidity of the ditch after the ditching operation, the current temperature and humidity, and the soil permeability of the ditch, and calculate the quality evaluation index of the ditch by combining the temperature deviation value and the humidity deviation value;

[0066] Step 5: Use the generated ditch quality evaluation index to correct the evaluation threshold to obtain the evaluation threshold correction value, compare the quality index with the evaluation threshold correction value, and determine whether the ditch meets the ditching quality standard.

[0067] It should be noted that by standardizing the feature data of different dimensions to the same scale range, the impact of feature value range differences on model weight learning is reduced, the convergence process of the model is accelerated, and the performance and prediction ability of the model are significantly improved. This preprocessing provides the model with cleaner and more uniform data input, laying the foundation for efficient model learning.

[0068] Therefore, it is necessary to pre-process the relevant characteristic data of the ditch including the ditch width, ditch depth, ditch length and ditch opening angle. The pre-processing includes characteristic data cleaning and data standardization. The method is as follows:

[0069] Data cleaning of feature data includes detection and deletion of outliers and duplicate data, and processing of missing values. Specifically, it uses statistical methods to identify outliers and duplicate data in feature data, deletes outliers and duplicate data in feature data, and uses the mean, median or mode of feature data to fill in missing values ​​in feature data.

[0070] The feature data is normalized as minimum-maximum normalization, scaling the data to Within the range, all feature data have the same scale, based on the formula:

[0071] ;

[0072] in, is the feature data after normalization. is the original feature data, is the minimum value of the same type of feature data in the data set. It is the maximum value of the same type of feature data in the dataset.

[0073] It should be noted that by quantifying the difference between the actual value and the standard value, an objective basis is provided for evaluating the quality of ditch construction. The setting of the deviation value is based on the standard value in the historical ditching data, which can reflect the degree of consistency between the actual ditch construction and the ideal design, and help to timely discover problems in construction and propose improvement plans. By clarifying the deviation value, the accuracy and consistency of ditch construction can be improved, thereby better meeting the needs of farmland drainage and flood control, and improving agricultural production efficiency and land resource utilization.

[0074] Therefore, it is necessary to calculate the deviations of ditch width, ditch depth, ditch length and ditch opening angle from their respective standard values, based on the following method:

[0075] The standard values ​​of ditch width, ditch depth and ditch opening angle after ditching are obtained from the historical ditching data, and the deviation values ​​of each parameter are calculated based on the following formula:

[0076] ;

[0077] ;

[0078] ;

[0079] ;

[0080] in, , , , Respectively represent the deviation values ​​of ditch width, depth, length and opening angle, , , , The collected ditch width, depth, length and opening angle data are respectively: , , , They are the standard values ​​of ditch width, depth, length and opening angle respectively.

[0081] It should be noted that by generating a feature vector from the deviation values ​​of ditch width, depth, length and opening angle and inputting it into the neural network model, quantitative evaluation and prediction of ditch quality can be achieved. The introduction of the model can not only improve the efficiency and accuracy of quality evaluation, but also replace the traditional subjective judgment that relies on manual experience, ensuring the objectivity and consistency of the evaluation results. At the same time, through the annotation training of historical data, the model can gradually optimize the ability to identify ditch construction problems and provide scientific guidance for future construction.

[0082] Therefore, it is necessary to construct a quality evaluation model based on the following methods:

[0083] The quality evaluation model adopts a neural network convolution structure, including an input layer, a hidden layer and an output layer. The input layer is used to receive the feature vector of the ditch, the hidden layer is used to process the feature vector of the ditch, and the output layer is used to output the predicted ditch quality index. , the larger the quality index, the better, which means that the quality standard of the ditch after the ditching operation is high and has more condition parameters for growing rapeseed;

[0084] The feature vectors of ditches formed by previous ditching operations are obtained, and the ditches are scored based on experts to obtain the ditch quality index. The feature vectors of ditches formed by historical ditching operations are used as input, and the ditch quality index is used as a label to train the quality evaluation model. During the training process, the mean square error function is selected as the loss function, and the loss function value is calculated based on the output result and the true label. The gradient is calculated through the back propagation algorithm, and the weights and bias of the neural network are updated. The above operation is repeated until the model reaches the predetermined number of training rounds.

[0085] It should be noted that by using temperature and humidity sensors to collect current temperature and humidity, and combining the best temperature and humidity conditions in historical data, the deviation value is calculated to achieve quantitative evaluation of ditch quality, and the construction quality and environmental adaptability of the ditch can be intuitively reflected by calculating quality evaluation indicators. After the construction is completed, it can be quickly determined whether the ditch has reached the best use conditions, providing a scientific basis for timely repair.

[0086] Therefore, it is necessary to calculate the quality evaluation index of the ditch, based on the following method:

[0087] Obtain the optimal temperature and humidity of the ditch after the ditching operation, and measure the current temperature and humidity by arranging the temperature and humidity sensors arranged on the ditch, and calculate the deviation between the current temperature, current humidity and the optimal temperature and humidity;

[0088] Calculation of ditch soil permeability using double ring infiltration method ;

[0089] The formula for calculating the quality evaluation index of the ditch is:

[0090] ;

[0091] in, It represents the quality evaluation index of the ditch. , is the current temperature and the current humidity, , To achieve the best temperature and humidity of the ditch after trenching. is the deviation between the current temperature and the optimal temperature, is the deviation between the current humidity and the optimal humidity, is the ditch soil permeability; in the above formula, the ditch soil permeability The larger the ditch quality evaluation index is, the The smaller it is, the more effectively the ditch can drain and infiltrate water, improving its overall operating efficiency; The larger the value is, the greater the deviation between the current temperature and the optimal temperature will be, which will lead to Increase, Increase means that the temperature of the ditch is too low, causing the soil to freeze, or the temperature is too high, causing the soil to dry and harden, which leads to a decrease in permeability and a decrease in quality evaluation indicators. Increase, the operation status and quality of the ditch are not ideal, on the contrary The smaller the value of The smaller it is, the better the running status and quality of the ditch are. The larger the value, The larger the value, the greater the deviation between the current humidity and the optimal humidity, which means that the humidity of the ditch is too low and the soil is cracked, or the humidity is too high and the soil is waterlogged, thus affecting the stability of the ditch. The index based on temperature reflects the quality evaluation index The reason for the large impact is that the impact of temperature change far exceeds that of humidity, and the impact of temperature is often nonlinear and changes dramatically with the deviation, which conforms to the characteristics of an exponential function.

[0092] It should be noted that by dynamically adjusting the threshold in combination with the quality index, the quality standard in the actual environment can be effectively reflected. Finally, by judging whether the ditch quality index reaches the correction threshold, it is clear whether the ditch meets the ditching quality standard. This method ensures the scientificity, accuracy and applicability of the evaluation process, provides a reliable basis for ditch construction and management, and helps to ensure project quality and operational safety.

[0093] Therefore, it is necessary to use the generated ditch quality evaluation index to correct the evaluation threshold to obtain the evaluation threshold correction value, and compare the quality index with the evaluation threshold correction value. The formula is as follows:

[0094] ;

[0095] in, represents the evaluation threshold correction value, is the evaluation threshold; in the above formula, the reason why The correction is made because the quality evaluation index The smaller the value, the better the ditch quality. No higher evaluation threshold is needed to evaluate the ditch quality index. The larger it is, the poorer the ditch quality is, and a higher evaluation threshold is needed to assess the ditch quality index;

[0096] According to the obtained evaluation threshold correction value, the size of the ditch quality index and the evaluation threshold correction value is judged based on the following logic:

[0097] ;

[0098] in, The logical value indicating the size of the ditch quality index and the evaluation threshold correction value. When , it indicates that the ditch quality index is less than the evaluation threshold correction value and does not meet the ditching quality standard; when , indicating that the ditch quality index is not less than the evaluation threshold correction value, and meets the ditching quality standard. is the ditch quality index.

[0099] The present invention also provides a ditching operation quality monitoring system based on rapeseed planting, and the quality monitoring system is used to execute the above-mentioned ditching operation quality monitoring method based on rapeseed planting, comprising:

[0100] A feature data collection module, wherein the feature data collection module is used to collect relevant feature data of the ditch after the ditching machine operates, wherein the relevant feature data includes ditch width, ditch depth, ditch length and ditch opening angle;

[0101] A deviation value calculation module, wherein the deviation value calculation module is used to calculate the deviation values ​​of the ditch width, ditch depth and ditch opening angle from their respective corresponding standard values ​​after preprocessing the relevant characteristic data of the ditch, and to generate a characteristic vector of the ditch according to the deviation values ​​between the characteristic data and the standard value, wherein the preprocessing includes characteristic data cleaning and data standardization processing;

[0102] A quality evaluation model and index calculation module, which is used to construct a quality evaluation model, and adopts a neural network convolution structure, including an input layer, a hidden layer and an output layer, wherein the input layer receives a feature vector, the hidden layer is responsible for data processing of the feature vector, and the output layer is used to output a ditch quality index, and the feature vector is input into the quality evaluation model to obtain the ditch quality index;

[0103] A quality index generation module, which is used to collect the optimal temperature and humidity of the ditch after the ditching operation and the current temperature and humidity as well as the soil permeability of the ditch, and calculate the quality evaluation index of the ditch by combining the temperature deviation value and the humidity deviation value;

[0104] The correction judgment module is used to correct the evaluation threshold value by using the generated ditch quality evaluation index to obtain the evaluation threshold correction value, compare the quality index with the evaluation threshold correction value, and judge whether the ditch meets the ditching quality standard.

[0105] The present invention further provides an electronic device, comprising:

[0106] A memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic equipment to execute the above-mentioned method for monitoring the quality of trenching operations based on rapeseed planting.

[0107] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0108] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0109] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0110] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for monitoring the quality of trenching operations based on rapeseed planting, characterized in that: The specific steps include: Step 1: Collect relevant characteristic data of the ditch after the trenching machine operates, wherein the relevant characteristic data includes ditch width, ditch depth, ditch length and ditch opening angle; Step 2: After preprocessing the relevant characteristic data of the ditch, the deviations of the ditch width, ditch depth and ditch opening angle from their corresponding standard values ​​are calculated respectively, and a characteristic vector of the ditch is generated according to the deviations between the characteristic data and the standard values. The preprocessing includes characteristic data cleaning and data standardization processing; Step 3: Construct a quality evaluation model using a convolutional neural network structure, including an input layer, a hidden layer, and an output layer, wherein the input layer receives a feature vector, the hidden layer is responsible for data processing of the feature vector, and the output layer is used to output a ditch quality index. The feature vector is input into the quality evaluation model to obtain the ditch quality index; Step 4: Collect the optimal temperature and humidity of the ditch after the ditching operation, the current temperature and humidity, and the soil permeability of the ditch, and calculate the quality evaluation index of the ditch by combining the temperature deviation value and the humidity deviation value; Step 5: Use the generated ditch quality evaluation index to correct the evaluation threshold to obtain the evaluation threshold correction value, compare the quality index with the evaluation threshold correction value, and determine whether the ditch meets the ditching quality standard.

2. A method for monitoring the quality of trenching operations based on rapeseed planting according to claim 1, characterized in that: The relevant characteristic data of the ditch including the ditch width, ditch depth, ditch length and ditch opening angle are preprocessed, and the preprocessing includes characteristic data cleaning and data standardization, and the method is based on: Data cleaning of feature data includes detection and deletion of outliers and duplicate data, and processing of missing values. Specifically, it uses statistical methods to identify outliers and duplicate data in feature data, deletes outliers and duplicate data in feature data, and uses the mean, median or mode of feature data to fill in missing values ​​in feature data. The feature data is normalized as minimum-maximum normalization, scaling the data to Within the range, all feature data have the same scale, based on the formula: ； in, is the feature data after normalization. is the original feature data, is the minimum value of the same type of feature data in the data set. It is the maximum value of the same type of feature data in the dataset.

3. The method for monitoring the quality of trenching work based on rapeseed planting according to claim 1, characterized in that: The deviations of ditch width, ditch depth, ditch length and ditch opening angle from their respective standard values ​​are calculated based on the following method: The standard values ​​of ditch width, ditch depth and ditch opening angle after ditching are obtained from the historical ditching data, and the deviation values ​​of each parameter are calculated based on the following formula: ； ； ； ； in, , , , Respectively represent the deviation values ​​of ditch width, depth, length and opening angle, , , , The collected ditch width, depth, length and opening angle data are respectively: , , , They are the standard values ​​of ditch width, depth, length and opening angle respectively.

4. The method for monitoring the quality of trenching work based on rapeseed planting according to claim 1, characterized in that: The quality evaluation model is constructed based on the following method: The quality evaluation model adopts a neural network convolution structure, including an input layer, a hidden layer and an output layer. The input layer is used to receive the feature vector of the ditch, the hidden layer is used to process the feature vector of the ditch, and the output layer is used to output the ditch quality index. ; The mean square error function is selected as the loss function, the loss function value is calculated according to the output result and the true label, and the gradient is calculated through the back propagation algorithm to update the weights and bias of the neural network. The above operation is repeated until the model reaches the predetermined number of training rounds.

5. The method for monitoring the quality of trenching work based on rapeseed planting according to claim 1, characterized in that: The method for calculating the quality evaluation index of the ditch is based on: Obtain the optimal temperature and humidity of the ditch after the ditching operation, and measure the current temperature and humidity by arranging the temperature and humidity sensors arranged on the ditch, and calculate the deviation between the current temperature, current humidity and the optimal temperature and humidity; Calculation of ditch soil permeability using double ring infiltration method ; The formula for calculating the quality evaluation index of the ditch is: ； in, It represents the quality evaluation index of the ditch. , is the current temperature and the current humidity, , To achieve the best temperature and humidity of the ditch after trenching. is the deviation between the current temperature and the optimal temperature, is the deviation between the current humidity and the optimal humidity, is the ditch soil permeability.

6. A method for monitoring the quality of trenching work based on rapeseed planting according to claim 5, characterized in that: The evaluation threshold is corrected using the generated ditch quality evaluation index to obtain the evaluation threshold correction value. The quality index is compared with the evaluation threshold correction value based on the following formula: ； in, represents the evaluation threshold correction value, is the evaluation threshold; According to the obtained evaluation threshold correction value, the size of the ditch quality index and the evaluation threshold correction value is judged based on the following logic: ； in, The logical value indicating the size of the ditch quality index and the evaluation threshold correction value. When , it indicates that the ditch quality index is less than the evaluation threshold correction value and does not meet the ditching quality standard; when , indicating that the ditch quality index is not less than the evaluation threshold correction value, and meets the ditching quality standard. is the ditch quality index.

7. A trenching quality monitoring system based on rapeseed planting, characterized in that: The quality monitoring system is used to execute the method for monitoring the quality of trenching operations based on rapeseed planting according to any one of claims 1 to 6, comprising: A feature data collection module, wherein the feature data collection module is used to collect relevant feature data of the ditch after the ditching machine operates, wherein the relevant feature data includes ditch width, ditch depth, ditch length and ditch opening angle; A deviation value calculation module, wherein the deviation value calculation module is used to calculate the deviation values ​​of the ditch width, ditch depth and ditch opening angle from their respective corresponding standard values ​​after preprocessing the relevant characteristic data of the ditch, and to generate a characteristic vector of the ditch according to the deviation values ​​between the characteristic data and the standard value, wherein the preprocessing includes characteristic data cleaning and data standardization processing; A quality evaluation model and index calculation module, which is used to construct a quality evaluation model, and adopts a neural network convolution structure, including an input layer, a hidden layer and an output layer, wherein the input layer receives a feature vector, the hidden layer is responsible for data processing of the feature vector, and the output layer is used to output a ditch quality index, and the feature vector is input into the quality evaluation model to obtain the ditch quality index; A quality index generation module, which is used to collect the optimal temperature and humidity of the ditch after the ditching operation and the current temperature and humidity as well as the soil permeability of the ditch, and calculate the quality evaluation index of the ditch by combining the temperature deviation value and the humidity deviation value; The correction judgment module is used to correct the evaluation threshold value by using the generated ditch quality evaluation index to obtain the evaluation threshold correction value, compare the quality index with the evaluation threshold correction value, and judge whether the ditch meets the ditching quality standard.

8. An electronic device, characterized in that: include: A memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic equipment to execute the method for monitoring the quality of trenching operations based on rapeseed planting as described in any one of claims 1-6.

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