CN115047207A - Early warning method for vehicle sinking of unmanned agricultural machine - Google Patents
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Abstract
The invention discloses an early warning method for the sinking of an unmanned agricultural machine, which comprises the following steps: s1, acquiring motion state data of the unmanned agricultural machine, representing the sudden change of the trapped agricultural machine by taking the speed of the agricultural machine as a state variable, respectively solving Spearman rank correlation coefficients of the dependent variables and the speed by adopting a correlation relationship method, and determining a control variable of a sudden change model; s2, establishing a normal operation-turning-down dovetail type mutation model of the agricultural machinery based on the dovetail type mutation model; s3, carrying out early warning of vehicle collapse according to a vehicle collapse dovetail type mutation model of normal operation of the agricultural machinery, and carrying out model inspection on error evaluation indexes; and S4, using the model for early warning of the actual agricultural machinery stuck in the vehicle. The invention designs the early warning mechanism of the sinking of the agricultural machinery by adopting the data and the mutation model of the unmanned agricultural machinery, can predict the sinking of the agricultural machinery in advance, and effectively reduces or even avoids the sinking accidents of the unmanned agricultural machinery.
Description
Technical Field
The invention belongs to the technical field of intelligent agricultural machinery, and particularly relates to an early warning method for vehicle sinking of an unmanned agricultural machine.
Background
China has rice planting area of about 4.5 hundred million mu throughout the year, and along with the continuous reduction of rural labor and the increase of production cost, the development of an agricultural unmanned technology is urgently needed to solve the problem of 'who comes' land. The unmanned agricultural machine easily causes the agricultural machine to sideslip and slide or even trap the vehicle in wet and slippery mud and paddy field environment with uneven height of a hard bottom layer, and if the unmanned agricultural machine is trapped, the unmanned agricultural machine cannot be timely removed from the vehicle or cannot be manually intervened in time, the unmanned agricultural machine can get deeper and cause operation accidents. The research and the related technology on the aspect of sinking the agricultural machinery are less at present. The Chinese invention patent CN 112462741 provides an unmanned agricultural machinery vehicle-trapping detection alarm system based on a cloud platform, wherein the slip rate is obtained by comparing the wheel speed and the vehicle speed, whether the agricultural machinery idles or not is judged, and then a vehicle-trapping alarm signal is generated through a 4G communication module. Chinese patents CN202514323U, CN212267166U and CN205631996U assist the agricultural machinery to fall into the vehicle for self rescue by designing corresponding mechanical structures.
Disclosure of Invention
The invention mainly aims to overcome the defects and shortcomings of the prior art, and provides an early warning method for the sinking of an unmanned agricultural machine.
In order to achieve the purpose, the invention adopts the following technical scheme:
an early warning method for a vehicle trap of an unmanned agricultural machine comprises the following steps:
s1, acquiring motion state data of the unmanned agricultural machine, representing the sudden change of the stuck agricultural machine by taking the speed v of the agricultural machine as a state variable, respectively solving a Spearman rank correlation coefficient with the speed v for each dependent variable by adopting a correlation method, and determining a control variable of a sudden change model;
s2, establishing a normal operation-turning-down dovetail type mutation model of the agricultural machinery based on the dovetail type mutation model;
s3, carrying out early warning of vehicle collapse according to a vehicle collapse dovetail type mutation model of normal operation of the agricultural machinery, carrying out model inspection on error evaluation indexes, and optimizing the mutation model;
and S4, using the optimized mutation model for early warning of actual agricultural machinery vehicle trapping.
Further, step S1 is specifically:
the method comprises the following steps that a plurality of dependent variables exist in the vehicle-trapping state of the unmanned agricultural machinery, and Spearman rank correlation coefficients are obtained for the dependent variables respectively by adopting a correlation relation method: note x i And y i Is agricultural machinery original position data, x' i And y' i The position data are respectively arranged according to the sequence from small to big:
where ρ is s Is rank correlation coefficient, n is data length;
respectively calculating rank correlation coefficients rho of each dependent variable and the speed v according to formula (1) s ，ρ s The larger the absolute value is, the more relevant the dependent variable is to the agricultural machinery vehicle-sinking sudden change.
Further, step S2 includes:
s21, designing a cusp line set of the dovetail mutation model;
s22, establishing an agricultural machinery vehicle trapping discriminant;
s23, establishing a normal operation-collapse dovetail type mutation model of the agricultural machinery.
Further, step S21 specifically includes:
the standard open-break potential function for dovetail mutations is:
wherein a, b and c are control variables, and x is a state variable;
and (3) obtaining a balance curved surface by derivation of the formula (2):
the set of mutation points also satisfies:
combining formula (3) and formula (4), eliminating x, and scoring the equation of fork set H;
from equation (3):
-x 4 -ax 2 -bx＝c (5)
taylor series expansion of F (X) at X:
wherein R is n (x) Is Lagrangian remainder;
according to the formula (6):
let the coefficients be:
wherein s is a constant;
performing transformation in a (p, q, r) three-dimensional space, and making x (p, q, r) equal to r, so as to obtain:
a(p,q,r)＝3q-6r 2
b(p,q,r)＝2p-6rq+8r 3 (9)
the prevalence of mutations set is:
{r,3q-6r 2 ,2p-6rq+8r 3 ,-2pr+3qr 2 -3r 4 } (10)
the singular set of the snap mapping x requires that p be 0, then the singular set is:
{r,3q-6r 2 ,-6rq+8r 3 ,3qr 2 -3r 4 } (11)
let q be 0, the three-fold term of the mutation prevalence set disappear, and the singular set is:
{r,-6r 2 ,8r 3 ,-3r 4 } (12)
the bifurcation set H is represented as:
{3q-6r 2 ,-6rq+8r 3 ,3qr 2 -3r 4 } (13)
the set of cusp lines for the dovetail mutation model is:
{a,b,c}＝{-6r 2 ,8r 3 ,-3r 4 } (14)。
further, step S22 specifically includes:
based on the symmetry characteristic of the bifurcation set, selecting a half plane with b being 0 and a being less than 0 for analysis, and rewriting the formula (3) as:
x 4 +ax 2 +c＝0 (15)
if the formula (15) is regarded as a completely flat mode, the agricultural machinery vehicle-sinking discriminant Δ is:
Δ＝a 2 -4c (16)
when delta is less than 0, the formula (16) has no solid root, and the mutation is shown, namely the agricultural machinery is trapped;
when delta is larger than 0, the formula (16) has a solid root, which indicates that no mutation occurs and the agricultural machinery is in a stable working state;
when Δ is 0, it indicates a critical state.
Further, step S23 is specifically:
by height difference delta h and roll angle
And transverse position deviation delta e is used as a control variable, the speed v of the agricultural machinery representing mutation is used as a state variable, and a dovetail type mutation model is establishedType, order:
wherein, A, B and C are dovetail mutation model parameters;
substituting the formula (17) into the formulas (2) to (16), namely establishing a breakdown potential function of the dovetail type sudden change model of the agricultural machinery for normal operation:
further, step S3 specifically includes:
s31, establishing an agricultural machinery vehicle-trapping early warning mechanism;
s32, evaluating the early warning accuracy of the agricultural machinery vehicle trap;
and S33, optimizing the particle swarm algorithm.
Further, step S31 is specifically:
setting the prediction step length as N, the data length as N, and calculating the speed v of the agricultural machine and the control variables delta h (i),
Substituting the sequence of delta e (i) into a formula (16) for training and judging, and carrying out data point classification on the dovetail type mutation model according to a judged result; when in the prediction time period t i ,t i +N]If delta < 0 exists in the agricultural machinery, the agricultural machinery is considered to be possibly stuck.
Further, step S32 is specifically:
in order to evaluate the accuracy of the early warning of the sinking of the agricultural machinery, a prediction evaluation method is designed:
wherein, P trap 、F trap 、HSS、PSS、N tp 、N fn 、N fp And N tn Respectively representing the correct rate, the false rate and the Heidke evaluation of the trapped vehicle judgmentThe method comprises the following steps of scoring, Pearce scoring, occurrence and correct early warning times of a trapped car, occurrence and wrong early warning times of the trapped car, non-occurrence and non-early warning times of the trapped car, and all evaluation index value ranges are [0,1 ]]；
In order to obtain maximum early warning accuracy, an objective function is designed:
f(p)＝max PSS (20)
the constraint conditions are as follows:
introduction of relaxation factor lambda 1 And λ 2 The formula (21) is changed to:
wherein an initial value λ is set 1 ＝0.7，λ 2 ＝0.3。
Further, step S33 is specifically:
and (3) performing objective function optimization solution on the formula (20) by adopting a particle swarm optimization algorithm and taking the formula (22) as measurement constraint, and repeatedly training and optimizing the trapping mutation model by evaluating the accuracy of prediction on experimental sample data to obtain the parameter values of the A, B and C models.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. according to the invention, information such as height difference, roll angle and transverse position deviation in the movement process of the agricultural machine, which is acquired by the unmanned agricultural machine pose sensing device, is adopted, a data driving method is adopted, the normal running operation and the stuck state of the agricultural machine are regarded as two stable states of an unmanned system based on a mutation theory, and the sudden transition threshold value from one stable state to the other stable state of the system is researched based on the mutation theory, so that the imminent stuck of the agricultural machine is judged, and the early warning of the stuck agricultural machine is realized.
2. The invention designs a vehicle trapping early warning mechanism aiming at the problem of vehicle trapping of the unmanned agricultural machine, realizes the parking of the unmanned agricultural machine and the waiting of manual rescue by remote alarm in time, can effectively avoid or reduce the probability of vehicle trapping accidents of the agricultural machine, and improves the operation safety of the unmanned agricultural machine.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a flow chart relating to the mutation model of the present invention;
fig. 3 is a schematic diagram of a sudden-change warning mechanism of a stuck vehicle according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
Examples
As shown in fig. 1 and 2, the invention provides an early warning method for the sinking of an unmanned agricultural machine, which comprises the following steps:
s1, acquiring the motion state data of the unmanned agricultural machine, representing the speed v of the vehicle-trapping mutation as a state variable, respectively calculating Spearman rank correlation coefficients of all dependent variables by adopting a correlation relation method, and determining a mutation model control variable; the method specifically comprises the following steps:
the unmanned agricultural machinery has a plurality of dependent variables in the state of sinking the vehicle, and x is recorded i And y i Respectively raw data, x' i And y' i Respectively calculating Spearman rank correlation coefficients of all the factors by adopting a correlation method for the data arranged from small to large respectively:
where ρ is s Is the rank correlation coefficient and n is the data length.
Respectively calculating rank correlation coefficients rho of each dependent variable and speed v according to formula (1) s ，ρ s The larger the absolute value, the more relevant the data is represented; in the embodiment, the elevation difference delta h and the roll angle are obtained by calculation
And lateral positionThe three factors of the deviation delta e are closely related to the trapped vehicles, and the influence is most obvious.
S2, establishing an agricultural machinery vehicle-trapping mutation model based on the dovetail mutation model; the method comprises the following steps:
s21, designing a cusp line set of the dovetail mutation model; the method comprises the following specific steps:
the standard opening potential function of the dovetail type mutation is
Wherein a, b and c are control variables, and x is a state variable.
The potential function of the formula (2) is derived to obtain a balanced curved surface:
the set of mutation points also satisfies:
combining formula (3) and formula (4), eliminating x, and scoring the equation of fork set H;
from equation (3):
-x 4 -ax 2 -bx＝c (5)
taylor series expansion of f (X) at X:
wherein R is n (x) Is a lagrange remainder.
According to the formula (6):
let the coefficients be:
where s is a constant, so a transform is made in (p, q, r) three-dimensional space, let x (p, q, r) be r, given:
a(p,q,r)＝3q-6r 2
b(p,q,r)＝2p-6rq+8r 3 (9)
then, the mutation prevalence set (i.e., equilibrium surface) is:
{r,3q-6r 2 ,2p-6rq+8r 3 ,-2pr+3qr 2 -3r 4 } (10)
the singular set of the snap mapping x requires that p be 0, then the singular set is:
{r,3q-6r 2 ,-6rq+8r 3 ,3qr 2 -3r 4 } (11)
let q be 0, the three-fold term of the mutation prevalence set disappear, and the singular set is:
{r,-6r 2 ,8r 3 ,-3r 4 } (12)
the set of forks H can be expressed as:
{3q-6r 2 ,-6rq+8r 3 ,3qr 2 -3r 4 } (13)
the set of cusp lines for the dovetail mutation model is:
{a,b,c}＝{-6r 2 ,8r 3 ,-3r 4 } (14)。
s22, establishing an unmanned agricultural machinery vehicle trapping discriminant; the method specifically comprises the following steps:
based on the symmetry characteristic of the bifurcation set, selecting a half plane with b being 0 and a being less than 0 for analysis, and rewriting the formula (3) as:
x 4 +ax 2 +c＝0 (15)
if equation (15) is considered to be a perfect flat mode, the discriminant Δ is:
Δ＝a 2 -4c (16)
when delta is less than 0, the formula (16) has no solid root and corresponds to the part above the swallow wing, and the system is shown to have sudden change, namely the unmanned agricultural machinery is trapped.
When the delta is more than 0, the formula (16) has a solid root which corresponds to the lower part of a swallow wing or a swallow tail part, and the system is not mutated and is in a stable working state.
When Δ is 0, the system is in a critical state.
S23, establishing a swallow-tail type mutation model of the unmanned agricultural machine; the method comprises the following specific steps:
by height difference delta h and roll angle
And the transverse position deviation delta e is used as a control variable, the speed v representing mutation is used as a state variable, a dovetail mutation model is established, and the following steps are performed:
wherein A, B and C are model parameters;
substituting the formula (17) into the formulas (2) to (16), namely establishing a driving potential function of a dovetail type sudden change model of the sinking vehicle for normal operation of the agricultural machinery:
s3, carrying out vehicle trapping early warning according to the vehicle trapping sudden change model discriminant, and carrying out model inspection on error evaluation indexes; the method comprises the following steps:
s31, establishing an early warning mechanism for the sinking of the unmanned agricultural machinery; the method specifically comprises the following steps:
as shown in FIG. 3, setting the prediction step length to be N, the data length to be N, the agricultural machine speed v and the control variables delta h (i),
And Δ e (i) into equation (16), and the model classifies data points according to the result of the training decision. When in the prediction time period [ t ] i ,t i +N]If delta < 0 exists in the agricultural machinery, the agricultural machinery is considered to be possibly stuck.
S32, carrying out an unmanned agricultural machinery vehicle trapping early warning accuracy evaluation method; the method specifically comprises the following steps:
in order to effectively evaluate the accuracy of the agricultural machinery vehicle-trapping early warning, a prediction evaluation method is designed:
wherein, P trap 、F trap 、HSS、PSS、N tp 、N fn 、N fp And N tn Respectively representing the vehicle trapping judgment accuracy, the misjudgment rate, the Heidke score, the Peirce score, the times of vehicle trapping and early warning accuracy, the times of vehicle trapping and early warning errors, the times of vehicle trapping and early warning failure and the times of vehicle trapping and failure of early warning, wherein all the evaluation index value ranges are [0,1 ]]。
In order to obtain maximum early warning accuracy, an objective function is designed:
f(p)＝max PSS (20)
the constraint conditions are as follows:
the constraint of equation (21) is too absolute, which tends to result in no solution for the objective function of equation (20). For this purpose, a relaxation factor λ is introduced 1 And λ 2 The formula (21) is changed to:
the agricultural machinery in paddy field is stuck in the vehicle is generally a small probability event, and in order to reduce the misjudgment of the stuck vehicle, the initial value lambda is set according to the experience 1 ＝0.7，λ 2 ＝0.3。
S33, optimizing a particle swarm algorithm; the method specifically comprises the following steps:
and (3) performing objective function optimization solution on the formula (20) by adopting a particle swarm optimization algorithm and taking the formula (22) as measurement constraint, and repeatedly training and optimizing the trapping sudden change prediction model by evaluating the accuracy of prediction on experimental sample data to obtain the parameter values of the A, B and C models.
The particle swarm optimization is specifically realized by the following steps:
taking the prediction accurate values as particles, taking all the prediction accurate values as particle swarms, initializing the positions and the speeds of all the particles in a population, and substituting the positions and the speeds into a formula (20) to calculate a particle fitness value;
evaluating the fitness of each particle, storing the position and the fitness value of each current particle, and screening the position and the fitness value of an optimal prediction accurate value;
updating the speed and position of the particles, comparing the fitness value of each particle with the optimal position of each particle, and if the fitness value is better, taking the fitness value as the optimal position of the current particle;
if the stopping condition is met (the preset precision or iteration times are reached), stopping searching, and outputting a result, otherwise, returning to the step III to continue iteration;
and fifthly, optimizing the vehicle trapping sudden change model to obtain the parameter values of the A, B and C models.
And S4, using the optimized model for early warning of actual agricultural machinery vehicle trapping.
According to the method, the unmanned agricultural machinery data and the mutation model are adopted to design the agricultural machinery vehicle-sinking early warning mechanism, the occurrence of the agricultural machinery vehicle-sinking can be predicted in advance, the occurrence of the accident that the unmanned agricultural machinery sinks is effectively reduced and even avoided, the safety of the operation of the unmanned agricultural machinery is improved, the method is high in adaptability, and the applicability of the unmanned agricultural machinery is effectively improved.
It should also be noted that in this specification, terms such as "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The unmanned agricultural machinery vehicle-trapping early warning method is characterized by comprising the following steps:
s1, acquiring motion state data of the unmanned agricultural machine, representing the sudden change of the stuck agricultural machine by taking the speed v of the agricultural machine as a state variable, respectively solving a Spearman rank correlation coefficient with the speed v for each dependent variable by adopting a correlation method, and determining a control variable of a sudden change model;
s2, establishing a normal operation-turning-down dovetail type mutation model of the agricultural machinery based on the dovetail type mutation model;
s3, carrying out vehicle trapping early warning according to a vehicle trapping dovetail type mutation model which is normal operation of the agricultural machinery, carrying out model inspection on error evaluation indexes, and optimizing the mutation model;
and S4, using the optimized mutation model for early warning of actual agricultural machinery vehicle trapping.
2. The unmanned agricultural machinery vehicle-trapping early warning method according to claim 1, wherein the step S1 is specifically:
the method comprises the following steps that a plurality of dependent variables exist in the state that the unmanned agricultural machine is trapped, and Spearman rank correlation coefficients are respectively obtained for the dependent variables by adopting a correlation method: note x i And y i As raw position data of the agricultural machine, x i ' and y i ' position data arranged in order from small to large are respectively:
wherein ρ s Is rank correlation coefficient, n is data length;
respectively calculating rank correlation coefficients rho of each dependent variable and speed v according to formula (1) s ，ρ s The larger the absolute value is, the more relevant the dependent variable is to the agricultural machinery vehicle-sinking sudden change.
3. The unmanned agricultural machinery vehicle-trapping early warning method according to claim 1, wherein the step S2 comprises:
s21, designing a cusp line set of the dovetail type mutation model;
s22, establishing an agricultural machinery vehicle trapping discriminant;
s23, establishing a normal operation-collapse dovetail type mutation model of the agricultural machinery.
4. The unmanned agricultural machinery vehicle-trapping early warning method according to claim 3, wherein the step S21 is specifically as follows:
the standard open-break potential function for dovetail mutations is:
wherein a, b and c are control variables, and x is a state variable;
and (3) obtaining a balance curved surface by derivation of the formula (2):
the set of mutation points also satisfies:
combining formula (3) and formula (4), eliminating x, and scoring the equation of fork set H; from equation (3):
-x 4 -ax 2 -bx＝c (5)
taylor series expansion of F (X) at X:
wherein R is n (x) Is Lagrangian remainder;
according to the formula (6):
let the coefficients be:
r(x,a,b)＝x
wherein s is a constant;
performing transformation in a (p, q, r) three-dimensional space, and making x (p, q, r) equal to r, so as to obtain:
a(p,q,r)＝3q-6r 2
b(p,q,r)＝2p-6rq+8r 3 (9)
the prevalence of mutations is then set as:
{r,3q-6r 2 ,2p-6rq+8r 3 ,-2pr+3qr 2 -3r 4 } (10)
the singular set requirement p of the mutation map x is 0, then the singular set is:
{r,3q-6r 2 ,-6rq+8r 3 ,3qr 2 -3r 4 } (11)
let q be 0, the three-fold term of the mutation prevalence set disappear, and the singular set is:
{r,-6r 2 ,8r 3 ,-3r 4 } (12)
the bifurcation set H is represented as:
{3q-6r 2 ,-6rq+8r 3 ,3qr 2 -3r 4 } (13)
the set of cusp lines for the dovetail mutation model is:
{a,b,c}＝{-6r 2 ,8r 3 ,-3r 4 } (14)。
5. the unmanned agricultural machinery vehicle-trapping early warning method according to claim 4, wherein the step S22 is specifically as follows:
based on the symmetry characteristic of the bifurcation set, selecting a half plane with b being 0 and a being less than 0 for analysis, and rewriting the formula (3) as:
x 4 +ax 2 +c＝0 (15)
when the formula (15) is regarded as a completely flat mode, the agricultural machinery vehicle-trapping discriminant Δ is:
Δ＝a 2 -4c (16)
when delta is less than 0, the formula (16) has no solid root, and the mutation is shown, namely the agricultural machinery is trapped;
when delta is larger than 0, the formula (16) has a solid root, which indicates that no mutation occurs and the agricultural machinery is in a stable working state;
when Δ is 0, it indicates a critical state.
6. The unmanned agricultural machinery vehicle-trapping early warning method according to claim 5, wherein the step S23 is specifically as follows:
by height difference delta h and roll angle
And the transverse position deviation delta e is used as a control variable, the agricultural machinery speed v representing mutation is used as a state variable, a dovetail type mutation model is established, and the following steps are performed:
a＝AΔh
c＝CΔe (17)
wherein, A, B and C are dovetail mutation model parameters;
substituting the formula (17) into the formulas (2) to (16), namely establishing a driving potential function of a dovetail type sudden change model of the sinking vehicle for normal operation of the agricultural machinery:
7. the unmanned agricultural machinery vehicle-trapping early warning method according to claim 6, wherein step S3 specifically comprises:
s31, establishing an agricultural machinery vehicle-trapping early warning mechanism;
s32, evaluating the early warning accuracy of the agricultural machinery vehicle trap;
and S33, optimizing the particle swarm algorithm.
8. The unmanned agricultural machinery vehicle-trapping early warning method according to claim 7, wherein the step S31 is specifically as follows:
setting the prediction step length as N, the data length as N, and calculating the speed v of the agricultural machine and the control variables delta h (i),
Substituting the sequence of delta e (i) into a formula (16) for training and judging, and carrying out data point classification on the dovetail mutation model according to a judged result; when in the prediction time period t i ,t i +N]If delta < 0 exists in the agricultural machinery, the agricultural machinery is considered to be possibly trapped.
9. The unmanned agricultural machinery vehicle-trapping early warning method according to claim 7, wherein the step S32 is specifically as follows:
in order to evaluate the accuracy of the early warning of the sinking of the agricultural machinery, a prediction evaluation method is designed:
wherein, P trap 、F trap 、HSS、PSS、N tp 、N fn 、N fp And N tn Respectively representing the vehicle trapping judgment accuracy, the misjudgment rate, the Heidke score, the Peirce score, the times of vehicle trapping and early warning accuracy, the times of vehicle trapping and early warning errors, the times of vehicle trapping and early warning failure and the times of vehicle trapping and failure of early warning, wherein all the evaluation index value ranges are [0,1 ]]；
In order to obtain maximum early warning accuracy, an objective function is designed:
f(p)＝max PSS (20)
the constraint conditions are as follows:
introduction of relaxation factor lambda 1 And λ 2 The formula (21) is changed to:
wherein an initial value λ is set 1 ＝0.7，λ 2 ＝0.3。
10. The unmanned agricultural machinery vehicle-trapping early warning method according to claim 7, wherein the step S33 is specifically as follows:
and (3) performing objective function optimization solution on the formula (20) by adopting a particle swarm optimization algorithm and taking the formula (22) as measurement constraint, and repeatedly training and optimizing the trapping mutation model by evaluating the accuracy of prediction on experimental sample data to obtain the parameter values of the A, B and C models.
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