System and method for automatically regulating and controlling rotary tillage depth of rotary cultivator based on soil hardness detection

Abstract

The invention discloses a system and a method for automatically regulating and controlling the rotary tillage depth of a rotary cultivator based on soil hardness detection, and relates to the field of automatic regulation and control systems. The rotary tillage depth of the rotary cultivator is optimized and adjusted by establishing a mapping model among the soil hardness factor of cultivated land, the working energy consumption of the rotary cultivator and the wear of a cutter; the cutter wear degree grade difference value and the energy consumption grade data after the rotary cultivator finishes working can meet the requirements, so that the working effect of the rotary cultivator is improved; abrasion degree grade difference values and energy consumption grade data of cutters of the rotary cultivators corresponding to multiple cultivated lands before and after working are calculated, a quantitative judgment basis is provided for follow-up judgment whether tilling depth data of the rotary cultivators need to be adjusted or not, and meanwhile an adjustment direction is provided for adjustment of the tilling depth data of the rotary cultivators. And the first final SVM classification model and the second final SVM classification model are constructed, so that the implementation feasibility and the operation simplicity are improved.

Description

Technical Field

[0001] The present invention belongs to the field of automatic control systems, and in particular, relates to an automatic control system and method for the tillage depth of a rotary tiller based on soil hardness detection. Background Art

[0002] The current rotary tiller equipment cannot adjust its tillage depth according to the soil conditions under different soil conditions. Therefore, when faced with moist soil, hard soil, and soil with a large amount of gravel or lumpy stones, the rotary tiller blades may be damaged, energy consumption may increase, or even the rotary tiller may be damaged due to improper pre-setting of the rotary tillage depth, which may lead to interruption of the tillage process or poor results, and increased maintenance costs of the rotary tiller. Summary of the invention

[0003] In view of the problems in the related art, the present invention proposes a system and method for automatically controlling the tillage depth of a rotary tiller based on soil hardness detection to overcome the above-mentioned technical problems existing in the existing related art.

[0004] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0005] The present invention is a method for automatically controlling the rotary tillage depth of a rotary tiller based on soil hardness detection, comprising the following steps:

[0006] S1. Collect average soil moisture data, average soil hardness data and average soil texture grade data of multiple cultivated lands to obtain average soil moisture data set, average soil hardness data set and average soil texture grade data set; and collect tillage depth requirement data of crops corresponding to multiple cultivated lands to obtain crop tillage depth data set;

[0007] S2, calculating the wear level difference and energy consumption level data of the tools of the rotary tillers corresponding to the plurality of cultivated fields before and after operation, and obtaining an initial tool wear level difference data set and an initial energy consumption level data set;

[0008] S3, using the initial tool wear level difference data set, the initial energy consumption level data set, the soil moisture average data set, the soil hardness average data set and the soil texture level average data set to construct a first final SVM classification model and a second final SVM classification model; and optimizing and adjusting the initial tillage depth data of the rotary tillers corresponding to the plurality of cultivated fields in conjunction with the initial tool wear level difference data set, the initial energy consumption level data set, the soil moisture average data set, the soil hardness average data set, the soil texture level average data set, the first final SVM classification model, the second final SVM classification model and the crop tillage depth data set to obtain a final tillage depth data set;

[0009] S4, adjusting parameters of rotary tillers corresponding to a plurality of cultivated fields according to the final tillage depth data set;

[0010] Since the soil moisture, soil hardness and soil texture of the cultivated land have a great influence on the rotary tillage of the cultivated land by the rotary tiller, the average soil moisture data, average soil hardness data and average soil texture grade data of multiple cultivated lands are collected in this scheme to provide data support for the subsequent construction of the first final SVM classification model and the second final SVM classification model that can map the soil moisture, soil hardness, soil texture and tillage depth data to the tool wear grade difference data and energy consumption grade data; and then by collecting the tillage depth requirement data of the crops corresponding to multiple cultivated lands, the adjustment range is determined for the subsequent adjustment of the tillage depth data of the rotary tiller, so that the adjusted tillage depth data can better meet the actual needs; the tool wear degree and energy consumption data of the rotary tiller can reflect the advantages and disadvantages of the current tillage depth data of the rotary tiller. The smaller the tool wear degree and energy consumption data of the rotary tiller are, the better the working effect of the current rotary tiller is; based on the above, in this scheme, the tools of the rotary tillers corresponding to multiple cultivated lands are calculated. The wear degree difference and energy consumption level data before and after work provide a quantitative basis for determining whether the tillage depth data of the rotary tiller needs to be adjusted, and also provide an adjustment direction for adjusting the tillage depth data of the rotary tiller; by constructing the first final SVM classification model and the second final SVM classification model, a mapping model between soil data and rotary tillage depth data to tool wear degree difference and energy consumption level data is established, so that when the tillage depth data of the rotary tiller is subsequently adjusted, there is no need to actually measure the tool wear degree difference and energy consumption level data of the rotary tiller to detect the quality of the adjusted data, thereby improving the feasibility and ease of operation of this scheme; by adjusting the tillage depth data of the rotary tiller whose tool wear degree difference and energy consumption level data do not meet the requirements, the tool wear degree difference and energy consumption level data of the rotary tiller after the work is completed can meet the requirements, thereby improving the working effect of the rotary tiller and reducing the maintenance cost of the rotary tiller.

[0011] Preferably, the S1 comprises the following steps:

[0012] S11, setting multiple pieces of arable land of the same size that need to be rotary tilled and corresponding rotary tillers of the same type and quality, and obtaining the arable land sample set a = {a1, a2, ..., a i ,...,a a′} and rotary tiller sample set, a i represents the i-th piece of cultivated land sample that needs to be rotary tilled, and a′ represents the total number of cultivated land samples that need to be rotary tilled;

[0013] S12, measuring the cultivated land sample set a={a1, a2, ..., a i ,...,a a′ The average soil moisture data, average soil hardness data and average soil texture grade data of each cultivated land sample in} are obtained to obtain the average soil moisture data set Soil hardness average data set and soil texture class average dataset Respectively represent the average data of soil moisture, average data of soil hardness and average data of soil texture grade corresponding to the i-th piece of cultivated land sample that needs to be rotary tilled in the cultivated land sample set;

[0014] S13, set the cultivated land sample set a={a1, a2, ..., a i ,...,a a′}, and obtain the crop tillage depth data set The farmland sample set a = {a1, a2, ..., a i ,...,a a′ The tillage depth requirement data of the crops planted on the i-th cultivated land sample in};

[0015] By setting multiple plots of land of the same size that need to be rotary tilled and corresponding rotary tillers of the same type and quality, it is ensured that subsequent data measurements and adjustments to tillage depth data are performed under the same premise, making the final adjustment results more reliable and accurate.

[0016] Preferably, the S12 comprises the following steps:

[0017] S121, in the cultivated land sample set a={a1, a2, ..., a i ,...,a a′}, multiple soil collection points are evenly set on each cultivated land sample to obtain the soil collection point matrix b; as follows,

[0018]

[0019] Among them, b ij represents the jth soil collection point set on the i-th cultivated land sample in the cultivated land sample set;

[0020] S122, collecting soil samples from the corresponding cultivated land samples in the cultivated land sample set according to the soil collection point matrix b, obtaining a soil sample matrix as follows,

[0021]

[0022] in, represents the soil sample collected at the jth soil collection point set on the i-th cultivated land sample in the cultivated land sample set;

[0023] S123, using a resistive soil moisture sensor to measure the soil sample matrix The humidity data of each soil sample is measured to obtain a soil humidity data matrix; a soil hardness meter is used to measure the soil sample matrix The hardness data of each soil sample is measured to obtain a soil hardness data matrix; the average value of each row of data in the soil moisture data matrix and the soil hardness data matrix is ​​calculated to obtain a soil moisture average data set and soil hardness average dataset

[0024] Measuring the soil sample matrix The average data of the texture grade of each soil sample in each cultivated land sample is obtained to obtain the average data set of soil texture grade;

[0025] By evenly setting multiple soil collection points on each cultivated land sample to collect soil samples, the randomness of soil data obtained in subsequent measurements is reduced, thereby improving the accuracy of the measurement.

[0026] Preferably, the soil sample matrix is ​​measured in S123 The average data of the texture grade of each soil sample in each cultivated land sample is obtained, and the average data set of soil texture grade is obtained, which includes the following steps:

[0027] S1231, measuring the soil sample matrix The mass percentage data of sand, silt and clay in each soil sample are obtained to obtain the mass percentage data set matrix as follows,

[0028]

[0029] in, Respectively represent the mass percentage data of sand particles, silt particles and clay particles in the soil sample collected at the j-th soil collection point set on the i-th cultivated land sample in the cultivated land sample set;

[0030] Calculate the mass percentage dataset matrix The average of the mass percentage data of sand, silt and clay in each row of data is obtained to obtain the mass percentage average data set

[0031] c={(c 11 ,c 12 ,c 13 ),(c 21 ,c 22 ,c23 ),...,(c i1 ,c i2 ,c i3 ),...,(c a′1 ,c a′2 ,c a′3 )},c i1 、c i2 、c i3 They respectively represent the average values ​​of the mass percentage data of sand particles, silt particles and clay particles in the soil samples collected at each soil collection point on the i-th cultivated land sample in the cultivated land sample set; the calculation formula is as follows:

[0032]

[0033] S1232, set a soil texture grade set; perform a clustering operation on the mass percentage average data set to obtain a mass percentage average data classification matrix set c i ′ represents the i-th mass percentage average data classification matrix obtained by clustering the mass percentage average data set; as follows,

[0034]

[0035] Among them, c i ' j1 、c i ' j2 、c i ' j3 Respectively represent c i The mass percentage data of sand, silt and clay in the jth soil sample in d i Indicates clustering to c i The total number of soil samples in ′;

[0036] S1233, classifying the mass percentage average data matrix set according to the soil texture grade set The soil texture grade of each mass percentage average data in the classification matrix is ​​set to obtain the soil texture grade average data set

[0037]

[0038] By clustering the mass percentage average data set, similar soil textures are clustered into one category, and then the soil texture data clustered into one category are set to the same level, thereby reducing the complexity of the soil texture level data and facilitating subsequent analysis.

[0039] Preferably, in S1232, a clustering operation is performed on the mass percentage average data set to obtain a mass percentage average data classification matrix set The following steps are involved:

[0040] S12321. Establish the first squirrel population d1′ i represents the i-th squirrel in the first squirrel population, represents the size of the first squirrel population; sets the maximum number of iterations of the first squirrel population to The current number of iterations is are respectively recorded as the first maximum number of iterations and the first current number of iterations; the search space dimension of the first squirrel population is

[0041] S12322, multiple times from the mass percentage data set matrix Randomly select several mass percentage data sets as the initial position matrix of each squirrel in the first squirrel population, and get the first initial position matrix set e 1i represents the initial position matrix of the i-th squirrel in the first squirrel population; as follows,

[0042]

[0043] Among them, e 1ij1 、e 1ij2 、e 1ij3 represent the components of the mass percentage data dimension of sand, silt and clay in the jth soil sample in the initial position matrix of the i-th squirrel in the first squirrel population respectively;

[0044] S12323, calculating the Euclidean distance between each mass percentage average data in the mass percentage average data set and each row of data in the initial position matrix of the i-th squirrel in the first squirrel population, and obtaining the Euclidean distance matrix e i ';as follows,

[0045]

[0046] Among them, e i ' i′j It represents the Euclidean distance between the i′th data in the mass percentage average data set and the jth row data in the initial position matrix of the i-th squirrel in the first squirrel population; the calculation formula is as follows,

[0047]

[0048] In the formula, c i′1 、c i′2 、c i′3Respectively represent the average values ​​of the mass percentage data of sand particles, silt particles and clay particles in the soil samples collected at each soil collection point on the i′th cultivated land sample in the cultivated land sample set;

[0049] According to the Euclidean distance matrix e i 'Construct the fitness function of the i-th squirrel in the first squirrel population as follows,

[0050]

[0051] S12324, start iteration, before iteration, set the first current iteration number Set to 1; the fitness function of the i-th squirrel is used in the first iteration Calculate the first initial position matrix set The fitness value of each initial position matrix in the first fitness value set is obtained to obtain a first fitness value set; the maximum fitness value in the first fitness value set and the corresponding initial position matrix are respectively used as the first global optimal fitness and the first global optimal position; the first initial position matrix set is adjusted according to the first global optimal fitness and the first global optimal position. After the update is completed, the first current iteration number is Add 1 and enter the next iteration;

[0052] In each other iteration, the fitness function of the i-th squirrel is used Calculate the fitness value of each position matrix updated in the previous round of iterations to obtain a second fitness value set; use the maximum fitness value and the corresponding position matrix in the second fitness value set as the second global optimal fitness and the second global optimal position respectively; continue to update each position matrix updated in the previous round of iterations according to the second global optimal fitness and the second global optimal position; after the update is completed, the first current iteration number is Add 1 and enter the next iteration;

[0053] S12325, when the When , the iteration is stopped and the first final global optimal position is output; the mass percentage average data set is classified according to the first final global optimal position to obtain the mass percentage average data classification matrix set

[0054] The squirrel optimization algorithm can effectively explore and utilize food resources in the search space by simulating the foraging strategy and gliding movement of squirrels, thereby achieving strong optimization ability; based on this, the squirrel optimization algorithm is used in this scheme to perform multiple iterative optimizations on the multiple cluster center data of the mass percentage average data set, and the overall discreteness of the mass percentage average data set is used as its fitness function. As the iteration proceeds, the overall discreteness of the mass percentage average data set becomes smaller and smaller, indicating that the multiple cluster center data of the mass percentage average data set searched are more accurate.

[0055] Preferably, S2 comprises the following steps:

[0056] S21, setting the wear degree levels of multiple rotary tiller cutters to obtain a cutter wear degree level set; measuring the cutter wear degree level data of each rotary tiller in the rotary tiller sample set before operation according to the cutter wear degree level set to obtain an initial cutter wear degree level data set;

[0057] According to the crop tillage depth dataset Set the initial tillage depth data and obtain the initial tillage depth data set represents the initial tillage depth data of the i-th rotary tiller in the rotary tiller sample set;

[0058] S22, setting multiple energy consumption levels and the tillage depth data of each rotary tiller during the working process are fixed to obtain an energy consumption level set; making each rotary tiller in the rotary tiller sample set start working, and after the work is completed, measuring the tool wear level data and energy consumption level data of each rotary tiller in the rotary tiller sample set according to the tool wear level set and the energy consumption level set to obtain a final tool wear level data set and an initial energy consumption level data set represents the energy consumption level data of the i-th rotary tiller in the rotary tiller sample set during operation;

[0059] The final tool wear level data set is subtracted from the initial tool wear level data set to obtain the initial tool wear level difference data set. The initial tool wear level difference data of the rotary tiller corresponding to the i-th piece of cultivated land sample that needs to be rotary tilled is set;

[0060] By setting multiple wear levels and energy consumption levels of rotary tiller tools, the complexity of the wear data and energy consumption data of the rotary tiller tools collected subsequently is reduced; by subtracting the final tool wear level data set from the initial tool wear level data set, the amount of wear on the rotary tiller tools during operation can be reflected.

[0061] Preferably, S3 comprises the following steps:

[0062] S31, setting a wear level difference data threshold and an energy consumption level data threshold;

[0063] S32, when the initial tool wear level difference data set There is an initial tool wear level difference data greater than or equal to the wear level difference data threshold or initial energy consumption level data set When the energy consumption level data in the data is greater than or equal to the energy consumption level data threshold, the crop tillage depth data set is satisfied. Under the premise of , the tillage depth data of the corresponding rotary tiller is adjusted until the initial tool wear level difference data set There is no initial tool wear level difference data greater than or equal to the wear level difference data threshold and the initial energy consumption level data set The final tillage depth data set is obtained until there is no energy consumption level data greater than or equal to the energy consumption level data threshold. represents the final tillage depth data of the i-th rotary tiller in the rotary tiller sample set;

[0064] Otherwise, there is no need to adjust the tillage depth data of the rotary tiller;

[0065] By setting the wear level difference data threshold and the energy consumption level data threshold, a comparison basis is set for the wear level difference data and the energy consumption level data of the rotary tiller, which provides an adjustment direction for the subsequent adjustment of the rotary tillage depth data of the rotary tiller.

[0066] Preferably, adjusting the tillage depth data of the corresponding rotary tiller in S32 includes the following steps:

[0067] S321, constructing a first initial SVM classification model and a second initial SVM classification model;

[0068] S322, using the initial tillage depth data set, the initial tool wear level difference data set, the soil moisture average data set, the soil hardness average data set, and the soil texture level average data set to train and test the first initial SVM classification model to obtain a first final SVM classification model;

[0069] The second initial SVM classification model is trained and tested using the initial tillage depth data set, the initial energy consumption level data set, the soil moisture average data set, the soil hardness average data set, and the soil texture level average data set to obtain a second final SVM classification model;

[0070] S323: adjusting the tillage depth data of the corresponding rotary tiller in cooperation with the first final SVM classification model and the second final SVM classification model to obtain a final tillage depth data set

[0071] Preferably, in S322, the initial tillage depth data set, the initial tool wear level difference data set, the soil moisture average data set, the soil hardness average data set and the soil texture level average data set are used to train and test the first initial SVM classification model, and obtaining the first final SVM classification model includes the following steps:

[0072] S32211, setting a first training data ratio; dividing the initial tillage depth data set, the initial tool wear level difference data set, the soil moisture average data set, the soil hardness average data set and the soil texture level average data set according to the first training data ratio to obtain a first initial tillage depth training data set, an initial tool wear level difference training data set, a first soil moisture average training data set, a first soil hardness average training data set, a first soil texture level average training data set, a first initial tillage depth test data set, an initial tool wear level difference test data set, a first soil moisture average test data set, a first soil hardness average test data set and a first soil texture level average test data set;

[0073] S32212, setting a first training error threshold; inputting the first initial tillage depth training data set, the initial tool wear level difference training data set, the first soil moisture average training data set, the first soil hardness average training data set, and the first soil texture level average training data set into the first initial SVM classification model for training; during the training process, when the training error is less than the first training error threshold, stopping the training to obtain the first trained SVM classification model; otherwise, continuing the training until the training error is less than the first training error threshold;

[0074] S32213, set a first test accuracy threshold; input the first initial tillage depth test data set, the initial tool wear level difference test data set, the first soil moisture average test data set, the first soil hardness average test data set and the first soil texture level average test data set into the first trained SVM classification model for testing; after the test is completed, obtain the first test accuracy; when the first test accuracy is greater than or equal to the first test accuracy threshold, use the first trained SVM classification model as the first final SVM classification model; otherwise, return to S3212 to continue training the first trained SVM classification model until the first test accuracy is greater than or equal to the first test accuracy threshold;

[0075] Preferably, in S322, the initial tillage depth data set, the initial energy consumption level data set, the soil moisture average data set, the soil hardness average data set, and the soil texture level average data set are used to train and test the second initial SVM classification model, and obtaining the second final SVM classification model includes the following steps:

[0076] S32221, setting a second training data ratio; dividing the initial tillage depth data set, the initial energy consumption level data set, the soil moisture average data set, the soil hardness average data set and the soil texture level average data set according to the second training data ratio to obtain a second initial tillage depth training data set, an initial energy consumption level training data set, a second soil moisture average training data set, a second soil hardness average training data set, a second soil texture level average training data set, a second initial tillage depth test data set, a second initial energy consumption level test data set, a second soil moisture average test data set, a second soil hardness average test data set and a second soil texture level average test data set;

[0077] S32222, setting a second training error threshold; inputting the second initial tillage depth training data set, the initial energy consumption level training data set, the second soil moisture average training data set, the second soil hardness average training data set, and the second soil texture level average training data set into the second initial SVM classification model for training; during the training process, when the training error is less than the second training error threshold, stopping the training to obtain a second trained SVM classification model; otherwise, continuing the training until the training error is less than the second training error threshold;

[0078] S32223, set a second test accuracy threshold; input the second initial tillage depth test data set, the initial energy consumption level test data set, the second soil moisture average test data set, the second soil hardness average test data set and the second soil texture level average test data set into the second trained SVM classification model for testing; after the test is completed, obtain the second test accuracy; when the second test accuracy is greater than or equal to the second test accuracy threshold, use the second trained SVM classification model as the second final SVM classification model; otherwise, return to S3222 to continue training the second trained SVM classification model until the second test accuracy is greater than or equal to the second test accuracy threshold;

[0079] By using the initial tillage depth data set, the initial tool wear level difference data set, the soil moisture average data set, the soil hardness average data set and the soil texture level average data set to train and test the first initial SVM classification model, the first final SVM classification model obtained has a good mapping ability between the initial tillage depth data set, the soil moisture average data set, the soil hardness average data set and the soil texture level average data set and the initial tool wear level difference data set; then using the initial tillage depth data set, the initial energy consumption level data set, the soil moisture average data set, the soil hardness average data set and the soil texture level average data set to train and test the second initial SVM classification model, the second final SVM classification model obtained has a good mapping ability between the initial tillage depth data set, the soil moisture average data set, the soil hardness average data set and the soil texture level average data set and the initial energy consumption level data set, thereby providing an accurate classification model for the subsequent tillage depth data of the rotary tiller.

[0080] Preferably, the S323 includes the following steps:

[0081] S3231, setting tillage depth data to be adjusted and corresponding tool wear level difference data f1′ to be optimized and energy consumption level data f2′ to be optimized, and obtaining soil moisture average data, soil hardness average data and soil texture level average data corresponding to tillage depth data to be adjusted in conjunction with the soil moisture average data set, soil hardness average data and soil texture level average data set, and recording them as soil moisture average adjustment data, soil hardness average adjustment data and soil texture level average adjustment data, respectively;

[0082] S3232, build the second squirrel population d2′ i represents the i-th squirrel in the second squirrel population, represents the size of the second squirrel population; sets the maximum number of iterations of the second squirrel population to The current number of iterations is are respectively recorded as the second maximum number of iterations and the second current number of iterations; the search space dimension of the second squirrel population is 1 dimension;

[0083] S3233, setting the initial position of each squirrel in the second squirrel population according to the tillage depth data to be adjusted, and obtaining an initial position set f i represents the initial position of the i-th squirrel in the second squirrel population; the calculation formula is as follows,

[0084] f i =f min +rand i ·(fmax -f min );

[0085] In the formula, f min 、f max They represent the lower limit and upper limit of the tillage depth data to be adjusted, respectively, and rand i For f i Generate a random number between 0 and 1;

[0086] S3234: construct the fitness function of the second squirrel population according to the tool wear level difference data f1′ to be optimized and the energy consumption level data f2′ to be optimized. as follows,

[0087]

[0088] In the formula, β is a positive number, indicating the correction parameter;

[0089] S3235, start iteration, before iteration, set the second current iteration number Set to 1; the fitness function of the second squirrel population is used in the first iteration And cooperate with the first final SVM classification model and the second final SVM classification model to calculate the initial position set The fitness value of each initial position in the third fitness value set is obtained; the maximum fitness value and the corresponding initial position in the third fitness value set are respectively used as the third global optimal fitness and the third global optimal position; the initial position set is adjusted according to the third global optimal fitness and the third global optimal position. After the update is completed, the second current iteration number Add 1 and enter the next iteration;

[0090] In each other iteration, the fitness function of the second squirrel population is used And cooperate with the first final SVM classification model and the second final SVM classification model to calculate the fitness value of each position updated in the previous round of iterations to obtain a fourth fitness value set; the maximum fitness value in the fourth fitness value set and the corresponding position matrix are used as the fourth global optimal fitness and the fourth global optimal position respectively; each position updated in the previous round of iterations is continued to be updated according to the fourth global optimal fitness and the fourth global optimal position; after the update is completed, the second current iteration number is Add 1 and enter the next iteration;

[0091] S3236, when the When , the iteration is stopped and the second final global optimal position is output; the corresponding initial tillage depth data in the initial tillage depth data set is replaced by the second final global optimal position to obtain the final tillage depth data set

[0092] The squirrel optimization algorithm is used to iteratively optimize the tillage depth of the rotary tiller that does not meet the requirements for multiple times, and the fitness function is constructed based on the tool wear level difference data and energy consumption level data of the rotary tiller. Therefore, as the iteration proceeds, the tool wear level difference data and energy consumption level data of the rotary tiller will become smaller and smaller, and eventually meet the set requirements.

[0093] Preferably, S4 comprises the following steps:

[0094] S41, adjusting the parameters of each rotary tiller in the rotary tiller sample set to change the rotary tillage depth, and after the adjustment is completed, using ultrasound to actually measure the rotary tillage depth of each rotary tiller in the rotary tiller sample set to obtain an actual rotary tillage depth data set;

[0095] S42, setting a rotary tillage depth error threshold; when the difference between the corresponding rotary tillage depth data in the actual rotary tillage depth data set and the final tillage depth data set is greater than or equal to the rotary tillage depth error threshold, return to S41 to continue parameter adjustment until the difference between the corresponding rotary tillage depth data in the actual rotary tillage depth data set and the final tillage depth data set is greater than or equal to the rotary tillage depth error threshold; otherwise, there is no need to return to S41 to continue parameter adjustment;

[0096] The parameters of the rotary tiller are adjusted through multiple cycles so that the error between the actual tillage depth data of the rotary tiller and the final tillage depth data obtained by previous optimization meets the requirements, thereby making the working effect of the adjusted rotary tiller better.

[0097] The automatic control system of rotary tillage depth of a rotary tiller based on soil hardness detection includes a cultivated land soil data measurement and calculation module, a crop tillage depth demand data collection module, a rotary tiller data measurement and calculation module, a classification model construction module, a tillage depth data adjustment module and a rotary tiller parameter adjustment module;

[0098] The cultivated land soil data measurement and calculation module is used to collect average soil moisture data, average soil hardness data and average soil texture grade data of multiple cultivated lands to obtain an average soil moisture data set, an average soil hardness data set and an average soil texture grade data set;

[0099] The crop tillage depth requirement data collection module is used to collect tillage depth requirement data of crops corresponding to a plurality of cultivated lands to obtain a crop tillage depth data set;

[0100] The rotary tiller data measurement and calculation module is used to calculate the wear level difference and energy consumption level data of the rotary tillers corresponding to multiple cultivated fields before and after work, and obtain an initial tool wear level difference data set and an initial energy consumption level data set;

[0101] The classification model building module is used to construct a first final SVM classification model and a second final SVM classification model using the initial tool wear level difference data set, the initial energy consumption level data set, the soil moisture average data set, the soil hardness average data set and the soil texture level average data set;

[0102] The tillage depth data adjustment module is used to optimize and adjust the initial tillage depth data of the rotary tiller corresponding to the plurality of cultivated fields in cooperation with the first final SVM classification model, the second final SVM classification model and the crop tillage depth data set to obtain a final tillage depth data set;

[0103] The rotary tiller parameter adjustment module is used to adjust the parameters of the rotary tillers corresponding to the multiple cultivated fields according to the final tillage depth data set.

[0104] The present invention has the following beneficial effects:

[0105] 1. In the present invention, a mapping model between the soil hardness factor of the cultivated land and the working energy consumption and tool wear of the rotary tiller is established to optimize and adjust the rotary tillage depth of the rotary tiller, so that the tool wear level difference and energy consumption level data of the rotary tiller after the work is completed can meet the requirements, thereby improving the working effect of the rotary tiller; wherein, the tool wear level difference and energy consumption level data of the rotary tillers corresponding to multiple cultivated lands before and after work are calculated, which provides a quantitative judgment basis for subsequent judgment on whether it is necessary to adjust the tillage depth data of the rotary tiller, and also provides an adjustment direction for adjusting the tillage depth data of the rotary tiller; constructing the first final SVM classification model and the second final SVM classification model improves the feasibility of implementation and ease of operation.

[0106] 2. In the present invention, the squirrel optimization algorithm is used to perform multiple iterative optimizations on multiple cluster center data of the mass percentage average data set and the tillage depth data of the rotary tiller. As the iterations proceed, the overall discreteness of the mass percentage average data set, the tool wear level difference data, and the energy consumption level data become smaller and smaller.

[0107] 3. In the present invention, by setting the wear level difference data threshold and the energy consumption level data threshold, a comparison basis is set for the wear level difference data and the energy consumption level data of the rotary tiller, which provides an adjustment direction for the subsequent adjustment of the rotary tillage depth data of the rotary tiller.

[0108] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0110] Figure 1 The present invention is a schematic diagram of a process of regulating the rotary tillage depth of a rotary tiller by the automatic control system for the rotary tillage depth of a rotary tiller based on soil hardness detection. DETAILED DESCRIPTION

[0111] The following will be combined with the drawings in the embodiments of the invention to clearly and completely describe the technical solutions in the embodiments of the invention. Obviously, the described embodiments are only part of the embodiments of the invention, not all of the embodiments. Based on the embodiments in the invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the invention.

[0112] In the description of the present invention, it is necessary to understand that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0113] Embodiment 1

[0114] This embodiment is a method for automatically controlling the tillage depth of a rotary tiller based on soil hardness detection, comprising the following steps:

[0115] S1. Collect average soil moisture data, average soil hardness data and average soil texture grade data of multiple cultivated lands to obtain average soil moisture data set, average soil hardness data set and average soil texture grade data set; and collect tillage depth requirement data of crops corresponding to multiple cultivated lands to obtain crop tillage depth data set;

[0116] The S1 comprises the following steps:

[0117] S11, setting multiple pieces of arable land of the same size that need to be rotary tilled and corresponding rotary tillers of the same type and quality, and obtaining the arable land sample set a = {a1, a2, ..., a i ,...,a a′} and rotary tiller sample set, a irepresents the i-th piece of cultivated land sample that needs to be rotary tilled, and a′ represents the total number of cultivated land samples that need to be rotary tilled;

[0118] S12, measuring the cultivated land sample set a={a1, a2, ..., a i ,...,a a′}, the average soil moisture data, average soil hardness data and average soil texture grade data of each cultivated land sample are obtained to obtain the average soil moisture data set a~1={a~ 11 ,a~ 12 ,...,a~ 1i ,...,a~ 1a′}, soil hardness average data set a~2 = {a~ 21 ,a~ 22 ,...,a~ 2i ,...,a~ 2a′} and soil texture class average dataset Respectively represent the average soil moisture data, the average soil hardness data and the average soil texture grade data corresponding to the i-th piece of cultivated land sample that needs to be rotary tilled in the cultivated land sample set;

[0119] The S12 comprises the following steps:

[0120] S121, in the cultivated land sample set a={a1, a2, ..., a i ,...,a a′}, multiple soil collection points are evenly set on each cultivated land sample to obtain the soil collection point matrix b; as follows,

[0121]

[0122] Among them, b ij represents the jth soil collection point set on the i-th cultivated land sample in the cultivated land sample set;

[0123] S122, collecting soil samples from the corresponding cultivated land samples in the cultivated land sample set according to the soil collection point matrix b, obtaining a soil sample matrix as follows,

[0124]

[0125] in, represents the soil sample collected at the jth soil collection point set on the i-th cultivated land sample in the cultivated land sample set;

[0126] S123, using a resistive soil moisture sensor to measure the soil sample matrix The humidity data of each soil sample is measured to obtain a soil humidity data matrix; a soil hardness meter is used to measure the soil sample matrix The hardness data of each soil sample is measured to obtain a soil hardness data matrix; the average value of each row of data in the soil moisture data matrix and the soil hardness data matrix is ​​calculated to obtain a soil moisture average data set and soil hardness average dataset

[0127] Measuring the soil sample matrix The average data of the texture grade of each soil sample in each cultivated land sample is obtained to obtain the average data set of soil texture grade;

[0128] S123 measures the soil sample matrix The average data of the texture grade of each soil sample in each cultivated land sample is obtained, and the average data set of soil texture grade is obtained, which includes the following steps:

[0129] S1231, measuring the soil sample matrix The mass percentage data of sand, silt and clay in each soil sample are obtained to obtain the mass percentage data set matrix as follows,

[0130]

[0131] in, Respectively represent the mass percentage data of sand particles, silt particles and clay particles in the soil sample collected at the j-th soil collection point set on the i-th cultivated land sample in the cultivated land sample set;

[0132] Calculate the mass percentage dataset matrix The average of the mass percentage data of sand, silt and clay in each row of data is obtained to obtain the mass percentage average data set

[0133] c={(c 11 ,c 12 ,c 13 ),(c 21 ,c 22 ,c 23 ),...,(c i1 ,c i2 ,c i3 ),...,(c a′1 ,c a′2 ,c a′3 )},c i1 、c i2 、c i3They respectively represent the average values ​​of the mass percentage data of sand particles, silt particles and clay particles in the soil samples collected at each soil collection point on the i-th cultivated land sample in the cultivated land sample set; the calculation formula is as follows:

[0134]

[0135] S1232, set a soil texture grade set; perform a clustering operation on the mass percentage average data set to obtain a mass percentage average data classification matrix set c i ′ represents the i-th mass percentage average data classification matrix obtained by clustering the mass percentage average data set; as follows,

[0136]

[0137] Among them, c i ' j1 、c i ' j2 、c i ' j3 Respectively represent c i The mass percentage data of sand, silt and clay in the jth soil sample in d i Indicates clustering to c i The total number of soil samples in ′;

[0138] In S1232, a clustering operation is performed on the mass percentage average data set to obtain a mass percentage average data classification matrix set. The following steps are involved:

[0139] S12321. Establish the first squirrel population d1′ i represents the i-th squirrel in the first squirrel population, represents the size of the first squirrel population; sets the maximum number of iterations of the first squirrel population to The current number of iterations is are respectively recorded as the first maximum number of iterations and the first current number of iterations; the search space dimension of the first squirrel population is

[0140] S12322, multiple times from the mass percentage data set matrix Randomly select several mass percentage data sets as the initial position matrix of each squirrel in the first squirrel population, and get the first initial position matrix set e 1i represents the initial position matrix of the i-th squirrel in the first squirrel population; as follows,

[0141]

[0142] Among them, e 1ij1 、e 1ij2 、e 1ij3 represent the components of the mass percentage data dimension of sand, silt and clay in the jth soil sample in the initial position matrix of the i-th squirrel in the first squirrel population respectively;

[0143] S12323, calculating the Euclidean distance between each mass percentage average data in the mass percentage average data set and each row of data in the initial position matrix of the i-th squirrel in the first squirrel population, and obtaining the Euclidean distance matrix e i ';as follows,

[0144]

[0145] Among them, e i ' i′j It represents the Euclidean distance between the i′th data in the mass percentage average data set and the jth row data in the initial position matrix of the i-th squirrel in the first squirrel population; the calculation formula is as follows,

[0146]

[0147] In the formula, c i′1 、c i′2 、c i′3 Respectively represent the average values ​​of the mass percentage data of sand particles, silt particles and clay particles in the soil samples collected at each soil collection point on the i′th cultivated land sample in the cultivated land sample set;

[0148] According to the Euclidean distance matrix e i 'Construct the fitness function of the i-th squirrel in the first squirrel population as follows,

[0149]

[0150] S12324, start iteration, before iteration, set the first current iteration number Set to 1; the fitness function of the i-th squirrel is used in the first iteration Calculate the first initial position matrix set The fitness value of each initial position matrix in the first fitness value set is obtained to obtain a first fitness value set; the maximum fitness value in the first fitness value set and the corresponding initial position matrix are respectively used as the first global optimal fitness and the first global optimal position; the first initial position matrix set is adjusted according to the first global optimal fitness and the first global optimal position. After the update is completed, the first current iteration number is Add 1 and enter the next iteration;

[0151] In each other iteration, the fitness function of the i-th squirrel is used Calculate the fitness value of each position matrix updated in the previous round of iterations to obtain a second fitness value set; use the maximum fitness value and the corresponding position matrix in the second fitness value set as the second global optimal fitness and the second global optimal position respectively; continue to update each position matrix updated in the previous round of iterations according to the second global optimal fitness and the second global optimal position; after the update is completed, the first current iteration number is Add 1 and enter the next iteration;

[0152] S12325, when the When , the iteration is stopped and the first final global optimal position is output; the mass percentage average data set is classified according to the first final global optimal position to obtain the mass percentage average data classification matrix set

[0153] S1233, classifying the mass percentage average data matrix set according to the soil texture grade set The soil texture grade of each mass percentage average data in the classification matrix is ​​set to obtain the soil texture grade average data set

[0154]

[0155] S13, set the cultivated land sample set a={a1, a2, ..., a i ,...,a a′}, and obtain the crop tillage depth data set The farmland sample set a = {a1, a2, ..., a i ,...,a a′ The tillage depth requirement data of the crops planted on the i-th cultivated land sample in};

[0156] S2, calculating the wear level difference and energy consumption level data of the tools of the rotary tillers corresponding to the plurality of cultivated fields before and after operation, and obtaining an initial tool wear level difference data set and an initial energy consumption level data set;

[0157] The S2 comprises the following steps:

[0158] S21, setting the wear degree levels of multiple rotary tiller cutters to obtain a cutter wear degree level set; measuring the cutter wear degree level data of each rotary tiller in the rotary tiller sample set before operation according to the cutter wear degree level set to obtain an initial cutter wear degree level data set;

[0159] According to the crop tillage depth dataset Set the initial tillage depth data and obtain the initial tillage depth data set represents the initial tillage depth data of the i-th rotary tiller in the rotary tiller sample set;

[0160] S22, setting multiple energy consumption levels and the tillage depth data of each rotary tiller during the working process are fixed to obtain an energy consumption level set; making each rotary tiller in the rotary tiller sample set start working, and after the work is completed, measuring the tool wear level data and energy consumption level data of each rotary tiller in the rotary tiller sample set according to the tool wear level set and the energy consumption level set to obtain a final tool wear level data set and an initial energy consumption level data set represents the energy consumption level data of the i-th rotary tiller in the rotary tiller sample set during operation;

[0161] The final tool wear level data set is subtracted from the initial tool wear level data set to obtain the initial tool wear level difference data set. The initial tool wear level difference data of the rotary tiller corresponding to the i-th piece of cultivated land sample that needs to be rotary tilled is set;

[0162] S3, using the initial tool wear level difference data set, the initial energy consumption level data set, the soil moisture average data set, the soil hardness average data set and the soil texture level average data set to construct a first final SVM classification model and a second final SVM classification model; and optimizing and adjusting the initial tillage depth data of the rotary tillers corresponding to the plurality of cultivated fields in conjunction with the initial tool wear level difference data set, the initial energy consumption level data set, the soil moisture average data set, the soil hardness average data set, the soil texture level average data set, the first final SVM classification model, the second final SVM classification model and the crop tillage depth data set to obtain a final tillage depth data set;

[0163] The S3 comprises the following steps:

[0164] S31, setting a wear level difference data threshold and an energy consumption level data threshold;

[0165] S32, when the initial tool wear level difference data set There is an initial tool wear level difference data greater than or equal to the wear level difference data threshold or initial energy consumption level data set When the energy consumption level data in the data is greater than or equal to the energy consumption level data threshold, the crop tillage depth data set is satisfied. Under the premise of , the tillage depth data of the corresponding rotary tiller is adjusted until the initial tool wear level difference data set There is no initial tool wear level difference data greater than or equal to the wear level difference data threshold and the initial energy consumption level data set The final tillage depth data set is obtained until there is no energy consumption level data greater than or equal to the energy consumption level data threshold. represents the final tillage depth data of the i-th rotary tiller in the rotary tiller sample set;

[0166] Otherwise, there is no need to adjust the tillage depth data of the rotary tiller;

[0167] The adjustment of the tillage depth data of the corresponding rotary tiller in S32 includes the following steps:

[0168] S321, constructing a first initial SVM classification model and a second initial SVM classification model;

[0169] S322, using the initial tillage depth data set, the initial tool wear level difference data set, the soil moisture average data set, the soil hardness average data set, and the soil texture level average data set to train and test the first initial SVM classification model to obtain a first final SVM classification model;

[0170] The second initial SVM classification model is trained and tested using the initial tillage depth data set, the initial energy consumption level data set, the soil moisture average data set, the soil hardness average data set, and the soil texture level average data set to obtain a second final SVM classification model;

[0171] In S322, the initial tillage depth data set, the initial tool wear level difference data set, the soil moisture average data set, the soil hardness average data set, and the soil texture level average data set are used to train and test the first initial SVM classification model, and obtaining the first final SVM classification model includes the following steps:

[0172] S32211, setting a first training data ratio; dividing the initial tillage depth data set, the initial tool wear level difference data set, the soil moisture average data set, the soil hardness average data set and the soil texture level average data set according to the first training data ratio to obtain a first initial tillage depth training data set, an initial tool wear level difference training data set, a first soil moisture average training data set, a first soil hardness average training data set, a first soil texture level average training data set, a first initial tillage depth test data set, an initial tool wear level difference test data set, a first soil moisture average test data set, a first soil hardness average test data set and a first soil texture level average test data set;

[0173] S32212, setting a first training error threshold; inputting the first initial tillage depth training data set, the initial tool wear level difference training data set, the first soil moisture average training data set, the first soil hardness average training data set, and the first soil texture level average training data set into the first initial SVM classification model for training; during the training process, when the training error is less than the first training error threshold, stopping the training to obtain the first trained SVM classification model; otherwise, continuing the training until the training error is less than the first training error threshold;

[0174] S32213, set a first test accuracy threshold; input the first initial tillage depth test data set, the initial tool wear level difference test data set, the first soil moisture average test data set, the first soil hardness average test data set and the first soil texture level average test data set into the first trained SVM classification model for testing; after the test is completed, obtain the first test accuracy; when the first test accuracy is greater than or equal to the first test accuracy threshold, use the first trained SVM classification model as the first final SVM classification model; otherwise, return to S3212 to continue training the first trained SVM classification model until the first test accuracy is greater than or equal to the first test accuracy threshold;

[0175] In S322, the initial tillage depth data set, the initial energy consumption level data set, the soil moisture average data set, the soil hardness average data set, and the soil texture level average data set are used to train and test the second initial SVM classification model, and obtaining the second final SVM classification model includes the following steps:

[0176] S32221, setting a second training data ratio; dividing the initial tillage depth data set, the initial energy consumption level data set, the soil moisture average data set, the soil hardness average data set and the soil texture level average data set according to the second training data ratio to obtain a second initial tillage depth training data set, an initial energy consumption level training data set, a second soil moisture average training data set, a second soil hardness average training data set, a second soil texture level average training data set, a second initial tillage depth test data set, a second initial energy consumption level test data set, a second soil moisture average test data set, a second soil hardness average test data set and a second soil texture level average test data set;

[0177] S32222, setting a second training error threshold; inputting the second initial tillage depth training data set, the initial energy consumption level training data set, the second soil moisture average training data set, the second soil hardness average training data set, and the second soil texture level average training data set into the second initial SVM classification model for training; during the training process, when the training error is less than the second training error threshold, stopping the training to obtain a second trained SVM classification model; otherwise, continuing the training until the training error is less than the second training error threshold;

[0178] S32223, set a second test accuracy threshold; input the second initial tillage depth test data set, the initial energy consumption level test data set, the second soil moisture average test data set, the second soil hardness average test data set and the second soil texture level average test data set into the second trained SVM classification model for testing; after the test is completed, obtain the second test accuracy; when the second test accuracy is greater than or equal to the second test accuracy threshold, use the second trained SVM classification model as the second final SVM classification model; otherwise, return to S3222 to continue training the second trained SVM classification model until the second test accuracy is greater than or equal to the second test accuracy threshold;

[0179] S323: adjusting the tillage depth data of the corresponding rotary tiller in cooperation with the first final SVM classification model and the second final SVM classification model to obtain a final tillage depth data set

[0180] The S323 comprises the following steps:

[0181] S3231, setting tillage depth data to be adjusted and corresponding tool wear level difference data f1′ to be optimized and energy consumption level data f2′ to be optimized, and obtaining soil moisture average data, soil hardness average data and soil texture level average data corresponding to tillage depth data to be adjusted in conjunction with the soil moisture average data set, soil hardness average data and soil texture level average data set, and recording them as soil moisture average adjustment data, soil hardness average adjustment data and soil texture level average adjustment data, respectively;

[0182] S3232, build the second squirrel population d2′ i represents the i-th squirrel in the second squirrel population, represents the size of the second squirrel population; sets the maximum number of iterations of the second squirrel population to The current number of iterations is are respectively recorded as the second maximum number of iterations and the second current number of iterations; the search space dimension of the second squirrel population is 1 dimension;

[0183] S3233, setting the initial position of each squirrel in the second squirrel population according to the tillage depth data to be adjusted, and obtaining an initial position set f i represents the initial position of the i-th squirrel in the second squirrel population; the calculation formula is as follows,

[0184] f i =f min +rand i ·(f max -f min );

[0185] In the formula, f min 、f max They represent the lower limit and upper limit of the tillage depth data to be adjusted, respectively, and rand i For f i Generate a random number between 0 and 1;

[0186] S3234: construct the fitness function of the second squirrel population according to the tool wear level difference data f1′ to be optimized and the energy consumption level data f2′ to be optimized. as follows,

[0187]

[0188] In the formula, β is a positive number, indicating the correction parameter;

[0189] S3235, start iteration, before iteration, set the second current iteration number Set to 1; the fitness function of the second squirrel population is used in the first iteration And cooperate with the first final SVM classification model and the second final SVM classification model to calculate the initial position set The fitness value of each initial position in the third fitness value set is obtained; the maximum fitness value and the corresponding initial position in the third fitness value set are respectively used as the third global optimal fitness and the third global optimal position; the initial position set is adjusted according to the third global optimal fitness and the third global optimal position. After the update is completed, the second current iteration number Add 1 and enter the next iteration;

[0190] In each other iteration, the fitness function of the second squirrel population is used And cooperate with the first final SVM classification model and the second final SVM classification model to calculate the fitness value of each position updated in the previous round of iterations to obtain a fourth fitness value set; the maximum fitness value in the fourth fitness value set and the corresponding position matrix are used as the fourth global optimal fitness and the fourth global optimal position respectively; each position updated in the previous round of iterations is continued to be updated according to the fourth global optimal fitness and the fourth global optimal position; after the update is completed, the second current iteration number is Add 1 and enter the next iteration;

[0191] S3236, when the When , the iteration is stopped and the second final global optimal position is output; the corresponding initial tillage depth data in the initial tillage depth data set is replaced by the second final global optimal position to obtain the final tillage depth data set

[0192] S4, adjusting parameters of rotary tillers corresponding to a plurality of cultivated fields according to the final tillage depth data set;

[0193] The S4 comprises the following steps:

[0194] S41, adjusting the parameters of each rotary tiller in the rotary tiller sample set to change the rotary tillage depth, and after the adjustment is completed, using ultrasound to actually measure the rotary tillage depth of each rotary tiller in the rotary tiller sample set to obtain an actual rotary tillage depth data set;

[0195] S42. Set a rotary tillage depth error threshold; when the difference between the corresponding rotary tillage depth data in the actual rotary tillage depth data set and the final tillage depth data set is greater than or equal to the rotary tillage depth error threshold, return to S41 to continue parameter adjustment until the difference between the corresponding rotary tillage depth data in the actual rotary tillage depth data set and the final tillage depth data set is greater than or equal to the rotary tillage depth error threshold; otherwise, there is no need to return to S41 to continue parameter adjustment.

[0196] Embodiment 2

[0197] This embodiment discloses a system for automatically controlling the tillage depth of a rotary tiller based on soil hardness detection. The system can implement the method of the above embodiment, including:

[0198] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0199] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A method for automatically controlling the tillage depth of a rotary tiller based on soil hardness detection, characterized in that: The following steps are involved: S1. Collect average soil moisture data, average soil hardness data and average soil texture grade data of multiple cultivated lands to obtain an average soil moisture data set, an average soil hardness data set and an average soil texture grade data set; And collect tillage depth requirement data of crops corresponding to multiple cultivated lands to obtain crop tillage depth data set; S2, calculating the wear level difference and energy consumption level data of the tools of the rotary tillers corresponding to the plurality of cultivated fields before and after operation, and obtaining an initial tool wear level difference data set and an initial energy consumption level data set; S3, constructing a first final SVM classification model and a second final SVM classification model using the initial tool wear level difference data set, the initial energy consumption level data set, the soil moisture average data set, the soil hardness average data set and the soil texture level average data set; And the initial tillage depth data of the rotary tillers corresponding to the multiple cultivated fields are optimized and adjusted in conjunction with the initial tool wear level difference data set, the initial energy consumption level data set, the soil moisture average data set, the soil hardness average data set, the soil texture level average data set, the first final SVM classification model, the second final SVM classification model and the crop tillage depth data set to obtain the final tillage depth data set; S4. Adjusting parameters of rotary tillers corresponding to a plurality of cultivated fields according to the final tillage depth data set.

2. The method for automatically controlling the tillage depth of a rotary tiller based on soil hardness detection according to claim 1 is characterized in that: The S1 comprises the following steps: S11, setting a plurality of pieces of cultivated land of the same size that need to be rotary tilled and corresponding rotary tillers of the same type and quality to obtain a cultivated land sample set and a rotary tiller sample set; S12, measuring the average soil moisture data, the average soil hardness data and the average soil texture grade data of each cultivated land sample in the cultivated land sample set to obtain an average soil moisture data set, an average soil hardness data set and an average soil texture grade data set; S13, setting tillage depth requirement data of crops planted in each piece of cultivated land sample in the cultivated land sample set, and obtaining a crop tillage depth data set.

3. The method for automatically controlling the tillage depth of a rotary tiller based on soil hardness detection according to claim 2 is characterized in that: The S12 comprises the following steps: S121, evenly setting a plurality of soil collection points on each cultivated land sample in the cultivated land sample set to obtain a soil collection point matrix; S122, collecting soil samples from corresponding cultivated land samples in the cultivated land sample set according to the soil collection point matrix to obtain a soil sample matrix; S123. Use a resistive soil moisture sensor to measure the moisture data of each soil sample in the soil sample matrix to obtain a soil moisture data matrix; use a soil hardness meter to measure the hardness data of each soil sample in the soil sample matrix to obtain a soil hardness data matrix; calculate the average value of each row of data in the soil moisture data matrix and the soil hardness data matrix to obtain a soil moisture average data set and a soil hardness average data set; measure the texture grade average data of each soil sample in each cultivated land sample in the soil sample matrix to obtain a soil texture grade average data set.

4. The method for automatically controlling the tillage depth of a rotary tiller based on soil hardness detection according to claim 3 is characterized in that: The S2 comprises the following steps: S21, setting the wear degree levels of multiple rotary tiller cutters to obtain a cutter wear degree level set; measuring the cutter wear degree level data of each rotary tiller in the rotary tiller sample set before operation according to the cutter wear degree level set to obtain an initial cutter wear degree level data set; setting initial tillage depth data according to the crop tillage depth data set to obtain an initial tillage depth data set; S22. Set multiple energy consumption levels to obtain an energy consumption level set; measure the tool wear level data and energy consumption level data of each rotary tiller in the rotary tiller sample set according to the tool wear level set and the energy consumption level set to obtain a final tool wear level data set and an initial energy consumption level data set; subtract the final tool wear level data set from the initial tool wear level data set to obtain an initial tool wear level difference data set.

5. The method for automatically controlling the tillage depth of a rotary tiller based on soil hardness detection according to claim 4 is characterized in that: The S3 comprises the following steps: S31, setting a wear level difference data threshold and an energy consumption level data threshold; S32. When the initial tool wear level difference data set contains initial tool wear level difference data greater than or equal to the wear level difference data threshold or the initial energy consumption level data set contains energy consumption level data greater than or equal to the energy consumption level data threshold, the tilling depth data of the corresponding rotary tiller is adjusted until the initial tool wear level difference data set contains no initial tool wear level difference data greater than or equal to the wear level difference data threshold and the initial energy consumption level data set contains no energy consumption level data greater than or equal to the energy consumption level data threshold, so as to obtain the final tilling depth data set; otherwise, there is no need to adjust the tilling depth data of the rotary tiller.

6. The method for automatically controlling the tillage depth of a rotary tiller based on soil hardness detection according to claim 5 is characterized in that: The adjustment of the tillage depth data of the corresponding rotary tiller in S32 includes the following steps: S321, constructing a first initial SVM classification model and a second initial SVM classification model; S322, using the initial tillage depth data set, the initial tool wear level difference data set, the soil moisture average data set, the soil hardness average data set and the soil texture level average data set to train and test the first initial SVM classification model to obtain a first final SVM classification model; using the initial tillage depth data set, the initial energy consumption level data set, the soil moisture average data set, the soil hardness average data set and the soil texture level average data set to train and test the second initial SVM classification model to obtain a second final SVM classification model; S323, adjusting the tillage depth data of the corresponding rotary tiller in cooperation with the first final SVM classification model and the second final SVM classification model to obtain a final tillage depth data set.

7. The method for automatically controlling the tillage depth of a rotary tiller based on soil hardness detection according to claim 6 is characterized in that: The S323 comprises the following steps: S3231, setting tillage depth data to be adjusted and corresponding tool wear level difference data to be optimized and energy consumption level data to be optimized, and obtaining soil moisture average data, soil hardness average data and soil texture level average data corresponding to tillage depth data to be adjusted in conjunction with the soil moisture average data set, soil hardness average data set and soil texture level average data set, and recording them as soil moisture average adjustment data, soil hardness average adjustment data and soil texture level average adjustment data, respectively; S3232, construct a second squirrel population; set the maximum number of iterations of the second squirrel population to The current number of iterations is are recorded as the second maximum number of iterations and the second current number of iterations respectively; S3233, setting the initial position of each squirrel in the second squirrel population according to the tillage depth data to be adjusted to obtain an initial position set; S3234, constructing a fitness function of the second squirrel population according to the tool wear level difference data to be optimized and the energy consumption level data to be optimized; S3235, start iteration; in each round of iteration, use the fitness function of the second squirrel population and cooperate with the first final SVM classification model, the second final SVM classification model, the soil moisture average adjustment data, the soil hardness average adjustment data and the soil texture grade average adjustment data to calculate the fitness value of each position updated in the previous round of iteration and continue to update each position updated in the previous round of iteration; S3236, when the When , the iteration is stopped and the second final global optimal position is output; the corresponding initial tillage depth data in the initial tillage depth data set is replaced by the second final global optimal position to obtain the final tillage depth data set.

8. The method for automatically controlling the tillage depth of a rotary tiller based on soil hardness detection according to claim 7 is characterized in that: The S4 comprises the following steps: S41, adjusting the parameters of each rotary tiller in the rotary tiller sample set to change the rotary tillage depth, and after the adjustment is completed, using ultrasound to actually measure the rotary tillage depth of each rotary tiller in the rotary tiller sample set to obtain an actual rotary tillage depth data set; S42. Set a rotary tillage depth error threshold; when the difference between the corresponding rotary tillage depth data in the actual rotary tillage depth data set and the final tillage depth data set is greater than or equal to the rotary tillage depth error threshold, return to S41 to continue parameter adjustment until the difference between the corresponding rotary tillage depth data in the actual rotary tillage depth data set and the final tillage depth data set is greater than or equal to the rotary tillage depth error threshold; otherwise, there is no need to return to S41 to continue parameter adjustment.

9. A system for realizing the method for automatically controlling the tillage depth of a rotary tiller based on soil hardness detection as described in any one of claims 1 to 8.

Images