Self-propelled sprayer scheduling method for soil mechanical compaction and reduction and water and fertilizer integration

Abstract

The invention discloses a self-propelled sprayer scheduling method for soil mechanical compaction and reduction and water and fertilizer integration, and relates to the field of agricultural machinery scheduling. The method aims at meeting the requirement for the compaction degree of the black soil in the field and reducing the dispatching cost. According to the self-propelled sprayer scheduling method for soil mechanical compaction reduction and water and fertilizer integration, a target function is constructed with the lowest total scheduling cost and the maximum compaction satisfaction degree after operation as targets according to the actual operation requirements of a self-propelled sprayer; and taking the target function as a fitness function of a bat algorithm, and scheduling the self-propelled sprayer by adopting the bat algorithm. The method is suitable for the field of agricultural machinery dispatching and outputs the dispatching scheme of the self-propelled sprayer.

Description

Technical Field

[0001] The invention belongs to the field of agricultural machinery dispatching. Background Art

[0002] Smart agriculture refers to the perception, transmission, storage, processing and control of the entire agricultural production process with the support of digital, networked and intelligent technologies, so as to realize the visualization of the agricultural production process, intelligent decision-making, precise operation and information management. Black soil is a high-quality and scarce arable land resource, and plays an irreplaceable role in maintaining national food security and stable economic and social development. However, due to long-term high-intensity development and utilization and unreasonable farming methods, the black soil in Northeast China is facing serious degradation problems. Although relevant departments have implemented relevant policies and regulations to promote the application of smart agriculture in black soil protection, protective farming has not yet achieved full coverage, the trend of soil erosion has not been fundamentally curbed, and the situation of black soil protection remains severe. Reducing the compaction of black soil can improve soil structure, increase soil fertility and enhance soil water and fertilizer retention capacity. The use of self-propelled sprayers that meet the soil compaction can achieve sustainable utilization and protection of black soil. Summary of the invention

[0003] The invention aims to meet the compaction requirements of black soil in fields and reduce the scheduling cost, and now provides a self-propelled sprayer scheduling method for soil mechanical compaction reduction and water-fertilizer integration.

[0004] A self-propelled sprayer scheduling method for reducing soil mechanical compaction and integrating water and fertilizer, including:

[0005] According to the actual operation requirements of the self-propelled sprayer, the objective function is constructed with the goal of minimizing the total scheduling cost and maximizing the post-operation compaction satisfaction;

[0006] The objective function is used as the fitness function of the bat algorithm, and the bat algorithm is used to schedule the self-propelled sprayer.

[0007] Furthermore, the above-mentioned use of bat algorithm to dispatch the self-propelled sprayer includes:

[0008] The pulse frequency r of the i-th bat in the t-th iteration is less than the random number rand1 i t When the estimated position X′ is calculated according to the position update formula i Otherwise, the estimated position X′ is calculated according to the random perturbation formula i ;

[0009] The position update formula is:

[0010] The random perturbation formula is:

[0011] In the formula, is the position of the i-th bat in the t-th iteration, is the flying speed of the i-th bat in the t+1th iteration, is the optimal position of all bats in the tth iteration, ε is a random number in [-1,1], is the pulse sound intensity of the i-th bat in the t-th iteration, the random number rand1∈[0,1];

[0012] The random number rand2 is less than the pulse intensity of the i-th bat in the t-th iteration. And estimate the position X′ i The fitness is less than the position When the fitness is i as the position of the i-th bat at the t+1th iteration And update the position of the i-th bat to position Otherwise, the i-th bat remains at position Do not move, end scheduling; random number rand2∈[0,1].

[0013] Furthermore, the update formula of the above flight speed is:

[0014]

[0015] Where ω is the inertia weight;

[0016] is the flying speed of the i-th bat in the t-th iteration;

[0017] is the search pulse frequency of the i-th bat in the t-th iteration, and:

[0018]

[0019] f min and f max are the lower and upper limits of the search pulse frequency respectively, and rand(·) is the random number generation function.

[0020] Furthermore, the expression of the above objective function is:

[0021] MinZ=αC t -βU,

[0022] Among them, C t is the total scheduling cost, U is the total compaction satisfaction, Z is the total scheduling target, α and β are C t and the coefficient of U.

[0023] Furthermore, the objective function expressions of the above total compaction satisfaction and total scheduling objectives are as follows:

[0024]

[0025] Among them, the path type set P = {1, 2}, when p = 1, it means that the path does not pass through the replenishment point, and when p = 2, it means that the path passes through the replenishment point;

[0026] Node set V = F∪R∪{O,D}, O is the starting point, D is the end point, F is the field set consisting of multiple farmlands in the target area, and R is the drug replenishment point set consisting of multiple drug replenishment points;

[0027] is a path decision variable, indicating whether to select a path from node g to node h of type p. otherwise

[0028] is the minimum moving cost from node g to node h under type p;

[0029] δ p is the type decision variable, when p = 2, δ p =1, otherwise δ p =0;

[0030] c r Fixed costs for refill operations;

[0031] is the operating cost of field n;

[0032] Intermediate variables L n To achieve the compaction capacity of the self-propelled sprayer after operation in field n, is the compaction satisfaction of field n.

[0033] Furthermore, in actual operation of the self-propelled sprayer, the constraints include:

[0034]

[0035] in, Indicates whether to select a path from starting point O to node h and type p. If yes, then otherwise

[0036] Indicates whether to select a path from node h to end point D and of type p. If yes, then otherwise

[0037] q g ,qh and q n are the remaining water levels of the liquid medicine tank when reaching node g, node h and field n, respectively, O is the initial water level of the liquid tank, and are the required liquid dosage for node g and field n respectively;

[0038] T O is the initial time, T h and T g are the arrival times of nodes h and g respectively, is the minimum transfer time from node g to node h under type p, is the minimum transfer time from node h to end point D under type p, and are the operation times of node g and node h respectively, t r A fixed time for the tonic operation;

[0039] N max is the maximum number of self-propelled sprayers available, Q max is the maximum capacity of the liquid tank, T max is the maximum operating time of the self-propelled sprayer;

[0040] L g The compaction capacity of the self-propelled sprayer after operation when reaching node g;

[0041] γ is the proportional coefficient between the compaction degree of the self-propelled sprayer and the water level in the tank after operation.

[0042] Furthermore, after the position of the i-th bat is updated, it also includes:

[0043] Find the bat position with the smallest fitness value in the t+1th iteration among all bats, and determine whether the fitness value of the bat position is less than the optimal position of all bats in the tth iteration The fitness value of

[0044] If yes, the bat position with the smallest fitness value is taken as the optimal position of all bats in the t+1th iteration.

[0045] Otherwise, the optimal positions of all bats in the tth iteration are As the optimal position of all bats in the t+1th iteration

[0046] Furthermore, the optimal positions of all bats in the t+1th iteration are obtained. After that, determine whether the iteration termination condition is reached.

[0047] If yes, then output the optimal path, total scheduling cost and total compaction satisfaction after the operation.

[0048] Otherwise, update the pulse frequency and pulse intensity and proceed to the next iteration.

[0049] Furthermore, the above iteration termination condition is to satisfy any of the following conditions:

[0050] I. Optimal Location Achieve the specified accuracy of the objective function;

[0051] II. t+1=T max .

[0052] Furthermore, the formula for updating the pulse frequency and pulse intensity is:

[0053]

[0054] In the formula, is the pulse frequency of the i-th bat in the t+1th iteration, is the pulse sound intensity of the i-th bat in the t+1-th iteration, η is the sound intensity attenuation coefficient, and γ is the frequency increase coefficient.

[0055] The present invention proposes a self-propelled sprayer scheduling method for soil mechanical compaction reduction and water-fertilizer integration, with the goal of meeting the black soil compaction requirements of the field and reducing the scheduling cost, and taking into account the fact that the self-propelled sprayer may run out of liquid during operation and need to go to the replenishment point to replenish the liquid. The present invention establishes a model whose objective function is to maximize the satisfaction of the black soil compaction degree of the field and minimize the total scheduling cost, and proposes relevant parameters and constraints; then, the solution steps of the model are designed based on the bat heuristic algorithm, and finally the optimal path and the objective function value are output.

[0056] The method of the present invention can output a scheduling plan for multiple self-propelled sprayers based on the two objectives of satisfying soil compaction and minimizing total scheduling cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A flow chart of the method for scheduling the operation of a self-propelled sprayer;

[0058] Figure 2 This is the flowchart of the bat algorithm. DETAILED DESCRIPTION

[0059] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0060] This implementation adopts the design principle of embedding the self-propelled sprayer scheduling problem into the bat algorithm. By simulating the foraging behavior of bats, the path planning, compaction satisfaction and cost minimization goals of the problem are converted into a fitness optimization problem.

[0061] Design:

[0062] 1. The position of each bat is used to represent the path planning scheme of the self-propelled sprayer. Specifically, it can be encoded as: I. The order of nodes passed by each path; II. Whether each path selects a replenishment point (path type). Through this encoding, the solution space of the self-propelled sprayer scheduling problem corresponds to the search space of the bat algorithm.

[0063] 2. The objective function is directly mapped to the fitness function of the bat algorithm, which is used to evaluate the pros and cons of each bat position. It includes two objectives: I. Maximizing compaction satisfaction: The satisfaction is calculated by the water level of the liquid tank when the self-propelled sprayer arrives at the field (converted into compaction); II. Minimizing the total cost: including path movement cost, operation cost and replenishment cost.

[0064] 3. The bat’s speed and position are updated to generate new path planning solutions.

[0065] Reference Figure 1 and Figure 2 Specifically describing this embodiment, the self-propelled sprayer scheduling method for soil mechanical compaction reduction and water-fertilizer integration described in this embodiment includes:

[0066] Step 1: Describe the problem of self-propelled sprayers and build a self-propelled sprayer scheduling model for soil mechanical compaction reduction and water-fertilizer integration.

[0067] 1. The parameters of the multi-task scheduling model of self-propelled sprayers include:

[0068] The field set F = {1, 2, ..., N} consisting of multiple farmlands in the target area;

[0069] A medicine replenishment point set R={1,2,...,M} consisting of multiple medicine replenishment points;

[0070] The path type set P = {1, 2} composed of multiple path types, when p = 1, it means that the path does not pass through the replenishment point, and when p = 2, it means that the path passes through the replenishment point;

[0071] All node sets V = F∪R∪{O,D}, O is the starting point and D is the end point;

[0072] Preset related constraints.

[0073] 2. Description of the problem of self-propelled sprayer

[0074] This implementation method aims to solve the problem of large-scale self-propelled sprayer operations that require going to replenishment points to replenish liquid medicine. Each field has certain requirements for soil compaction, so the task is to minimize the total scheduling cost and maximize the satisfaction of field compaction requirements.

[0075] For each field operation point, the location factors of the operation point and the drug supplementation point and the operation matching factors are comprehensively considered, and a path arc is constructed between the two nodes. The arcs passing through the two nodes are divided into two types:

[0076] (1) The route that does not pass through the drug replenishment point; (2) The route that passes through the drug replenishment point.

[0077] Each arc has three indicators: minimum path distance, minimum path time and minimum path cost. For every two nodes, different distances, times and costs can be obtained according to different arc types. If a replenishment point is visited between two nodes, the tank capacity of the self-propelled sprayer will be reset.

[0078] In addition, each field has four indicators: the amount of liquid medicine required, the degree of compaction, the operation time, and the operation cost. The maximum operation time is specified for each closed path. The sum of all path time, operation time, and drug replenishment time on this closed path cannot exceed the maximum operation time, which constrains the total operation time of each drug replenishment machine.

[0079] Step 2: Construct the objective function and the constraints of the objective function based on the problem described in step 1

[0080] A garage with multiple self-propelled sprayers with exactly the same parameters is the starting point O. It is necessary to spray multiple plots of land with different compaction requirements in the area. Each plot of land can only be served once. During the operation, if the remaining capacity of the liquid does not meet the liquid demand of the next plot of land, the self-propelled sprayer needs to go to the replenishment point to replenish the liquid before operating. In addition, the self-propelled sprayer needs to return to the garage within the specified maximum operating time. Therefore, the scheduling objectives of the self-propelled sprayer include: (1) Minimizing the total scheduling cost. The total scheduling cost is the sum of the moving cost, the operating cost, and the liquid replenishment cost. (2) Maximizing the total compaction satisfaction of each plot of land. Each plot of land has a compaction requirement. The compaction capacity of the self-propelled sprayer after operating on each plot of land is related to the water level of the liquid tank when it arrives at the plot of land.

[0081] 1. The expression of the objective function includes:

[0082] A. Total dispatch cost target of self-propelled sprayer:

[0083]

[0084] Among them, C t is the total dispatch cost; is a path decision variable, indicating whether to select a path from node g to node h of type p. otherwise is the minimum moving cost from node g to node h under type p; δ p is the type decision variable, when p = 2, δ p =1, otherwise δ p =0;c r Fixed costs for refill operations; is the operating cost of field n.

[0085] B. Post-operation compaction satisfaction target. When the post-operation compaction degree is less than the field compaction degree requirement, the satisfaction degree is 1:

[0086]

[0087] Among them, U is the total compaction satisfaction; L n To achieve the compaction capacity of the self-propelled sprayer after operation in field n, Compaction satisfaction of field n.

[0088] C. Overall dispatching goal, requiring the lowest overall dispatching cost and the highest overall compaction satisfaction after the operation

[0089] MinZ=αC t -βU,

[0090] Among them, Z is the overall scheduling target, α and β are C t and the coefficient of U.

[0091] 2. Constraints of the objective function

[0092] (1) Only self-propelled sprayers can flow out of the starting point O, and self-propelled sprayers can only start from the starting point; the number of self-propelled sprayers flowing out of the starting point O cannot be greater than the maximum number of available self-propelled sprayers, then there is a constraint:

[0093]

[0094] in, Indicates whether to select a path from starting point O to node h and type p. If yes, then otherwise N max The maximum number of self-propelled sprayers available.

[0095] (2) All self-propelled sprayers starting from the starting point O must eventually return to the end point D, so there is a constraint:

[0096]

[0097] in, Indicates whether to select a path from node h to end point D and of type p. If yes, then otherwise

[0098] (3) If the inflow and outflow of each field are equal and each field can only be visited once, then there is a constraint:

[0099]

[0100] (4) The path that flows directly out from the starting point O and the path that will flow directly into the end point will not pass through the replenishment point, so there is a constraint:

[0101]

[0102] (5) Constrain the dynamic change of the water level in the liquid medicine tank:

[0103]

[0104] Among them, q g and q h are the remaining water levels of the liquid medicine tank when reaching nodes g and h, respectively. is the amount of liquid medicine required for node g, Q max The maximum capacity of the liquid tank.

[0105] (6) When a certain field is reached, the water level in the liquid medicine tank is always greater than the liquid medicine demand of the field, then there is a constraint:

[0106]

[0107] Among them, q n is the remaining water level in the tank when reaching field n, The amount of liquid medicine required for field n.

[0108] (7) If the upper and lower limits of the water level in the tank are restricted, there are constraints:

[0109]

[0110] (8) If the initial water level of the liquid medicine tank is full, there are constraints:

[0111] q O =Q max ,

[0112] Among them, q O The initial water level of the liquid tank.

[0113] (9) Evaluate the arrival time of different nodes and impose the maximum duration constraint on the vehicle route, then:

[0114] T O =0,

[0115]

[0116] Among them, T O is the initial time, T h and T g are the arrival times of nodes h and g respectively, is the minimum transfer time from node g to node h under type p, is the minimum transfer time from node h to end point D under type p, and are the operation times of node g and node h respectively, t r is the fixed time of the replenishment operation, T max The maximum operating time of the self-propelled sprayer.

[0117] (10) The degree of compaction of a self-propelled sprayer after operation on a certain field is related to the water level of the liquid tank reaching the field, so there is a constraint:

[0118]

[0119] Among them, L g is the compaction capacity of the self-propelled sprayer after operation when reaching node g, and γ is the proportional coefficient of the compaction degree of the self-propelled sprayer after operation and the water level of the liquid tank (when arriving at the field).

[0120] (11) The values ​​of binary decision variables are constrained:

[0121]

[0122] δ p ∈{0,1}.

[0123] (12) The field compaction satisfaction, the time of arrival at the field, and the range of values ​​of the remaining water level in the liquid medicine tank when arriving at the field are constrained:

[0124]

[0125] Step 3: Use the bat algorithm to design the solution steps for the objective function to obtain the optimal path.

[0126] Initialization: Randomly generate the initial position of each bat The initial flight speed v of each bat i =0.

[0127] S1: Update bat position

[0128] Generate a random number rand1∈[0,1] and determine whether rand1 satisfies in, is the pulse frequency of the i-th bat in the t-th iteration.

[0129] If yes, calculate the position of the i-th bat in the t+1th iteration according to the position update formula Then execute S2.

[0130] The position update formula is:

[0131]

[0132] in, and are the positions of the i-th bat in the t+1th and tth iterations, respectively. is the flight speed of the i-th bat in the t+1-th iteration, which is obtained by the flight speed update formula:

[0133]

[0134] ω is the inertia weight; is the flying speed of the i-th bat in the t-th iteration; is the optimal position of all bats in the tth iteration; is the search pulse frequency of the i-th bat in the t-th iteration, and:

[0135]

[0136] f min and fmax are the lower and upper limits of the search pulse frequency respectively, and rand(·) is the random number generation function.

[0137] Otherwise, the optimal position of all bats in the tth iteration is Perform random perturbations to obtain the position of the i-th bat in the t+1th iteration Then execute S2.

[0138] The random perturbation formula is:

[0139]

[0140] Among them, ε is a random number in [-1,1], is the pulse sound intensity of the i-th bat in the t-th iteration.

[0141] S2: Determine whether to move to a new position

[0142] Generate a random number rand2∈[0,1] and determine whether it satisfies: And location The fitness value (i.e., the overall scheduling target) is less than the position The fitness value of Better than location Then the position of the i-th bat is updated to Otherwise, keep the i-th bat in its original position No move, end scheduling.

[0143] At this point, the above method can enable the self-propelled sprayer to find the next target position, that is, to achieve the scheduling of the self-propelled sprayer.

[0144] In actual work, the total path of the self-propelled sprayer can be obtained through the following steps.

[0145] S3: Update the optimal position

[0146] Find the bat position with the smallest fitness value in the t+1th iteration among all bats, and determine whether the fitness value of the bat position is better than that of the position The fitness value of .

[0147] If yes, the bat position with the smallest fitness value is taken as the optimal position of all bats in the t+1th iteration. Then execute S4;

[0148] Otherwise, As the optimal position of all bats in the t+1th iteration Then execute S4.

[0149] S4: Termination judgment

[0150] If any of the following conditions is met, execute S5, otherwise execute S6;

[0151] I. Optimal Location Achieve the specified accuracy of the objective function;

[0152] II. t+1=T max .

[0153] S5: Output results

[0154] The optimal path is output, and then the total scheduling cost and the total compaction satisfaction after the operation are calculated based on the optimal path.

[0155] S6: Update the pulse frequency and pulse intensity, then set t=t+1 and return to S1.

[0156] The new pulse frequency and pulse intensity update formula is:

[0157]

[0158] Among them, η is the sound intensity attenuation coefficient, and γ is the frequency increase coefficient.

[0159] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in a manner different from that described in the original claims. It should also be understood that the features described in conjunction with a single embodiment may be used in other described embodiments.

Claims

1. A self-propelled sprayer scheduling method for reducing soil mechanical compaction and integrating water and fertilizer, characterized in that: include: According to the actual operation requirements of the self-propelled sprayer, the objective function is constructed with the goal of minimizing the total scheduling cost and maximizing the post-operation compaction satisfaction; The objective function is used as the fitness function of the bat algorithm, and the bat algorithm is used to schedule the self-propelled sprayer.

2. The method for dispatching a self-propelled sprayer for reducing soil mechanical compaction and integrating water and fertilizer according to claim 1, characterized in that: The method of using the bat algorithm to dispatch the self-propelled sprayer includes: The pulse frequency r of the i-th bat in the t-th iteration is less than the random number rand1 i t When the estimated position X is calculated according to the position update formula i ′, otherwise the estimated position X is calculated according to the random perturbation formula i ′; The position update formula is: The random perturbation formula is: In the formula, is the position of the i-th bat in the t-th iteration, is the flying speed of the i-th bat in the t+1th iteration, is the optimal position of all bats in the tth iteration, ε is a random number in [-1,1], is the pulse sound intensity of the i-th bat in the t-th iteration, the random number rand1∈[0,1]; The random number rand2 is less than the pulse intensity of the i-th bat in the t-th iteration. And the estimated position X i ′ has a fitness less than position When the fitness is i ′ is the position of the i-th bat in the t+1th iteration And update the position of the i-th bat to position Otherwise, the i-th bat remains at position Do not move, end scheduling; random number rand2∈[0,1].

3. The method for dispatching a self-propelled sprayer for reducing soil mechanical compaction and integrating water and fertilizer according to claim 2, characterized in that: The update formula of the flight speed is: Where ω is the inertia weight; is the flying speed of the i-th bat in the t-th iteration; f i t is the search pulse frequency of the i-th bat in the t-th iteration, and: f i t =f min +(f max -f min )·rand(·), f min and f max are the lower and upper limits of the search pulse frequency respectively, and rand(·) is the random number generation function.

4. The method for dispatching a self-propelled sprayer for reducing soil mechanical compaction and integrating water and fertilizer according to claim 1, 2 or 3, characterized in that: The expression of the objective function is: MinZ=αC t -βU, Among them, C t is the total scheduling cost, U is the total compaction satisfaction, Z is the total scheduling target, α and β are C t and the coefficient of U.

5. The method for dispatching a self-propelled sprayer for reducing soil mechanical compaction and integrating water and fertilizer according to claim 4, characterized in that: The objective function expressions of the total compaction satisfaction and the total scheduling target are as follows: Among them, the path type set P = {1, 2}, when p = 1, it means that the path does not pass through the replenishment point, and when p = 2, it means that the path passes through the replenishment point; Node set V = F∪R∪{O,D}, O is the starting point, D is the end point, F is the field set consisting of multiple farmlands in the target area, and R is the drug replenishment point set consisting of multiple drug replenishment points; is a path decision variable, indicating whether to select a path from node g to node h of type p. otherwise g≠h; is the minimum moving cost from node g to node h under type p; δ p is the type decision variable, when p = 2, δ p =1, otherwise δ p =0; c r is the fixed cost of the medication replenishment operation, is the operating cost of field n; Intermediate variables L n To achieve the compaction capacity of the self-propelled sprayer after operation in field n, is the compaction satisfaction of field n.

6. The method for dispatching a self-propelled sprayer for reducing soil mechanical compaction and integrating water and fertilizer according to claim 5, characterized in that: In actual operation, the constraints of the self-propelled sprayer include: in, Indicates whether to select a path from starting point O to node h and type p. If yes, then otherwise Indicates whether to select a path from node h to end point D and of type p. If yes, then otherwise q g ,q h and q n are the remaining water levels of the liquid medicine tank when reaching node g, node h and field n, respectively, O is the initial water level of the liquid medicine tank, and are the required liquid dosage for node g and field n respectively; T O is the initial time, T h and T g are the arrival times of nodes h and g respectively, is the minimum transfer time from node g to node h under type p, is the minimum transfer time from node h to end point D under type p, and are the operation times of node g and node h respectively, t r A fixed time for the tonic operation; N max is the maximum number of self-propelled sprayers available, Q max is the maximum capacity of the liquid tank, T max is the maximum operating time of the self-propelled sprayer; L g The compaction capacity of the self-propelled sprayer after operation when reaching node g; γ is the proportional coefficient between the compaction degree of the self-propelled sprayer and the water level in the tank after operation.

7. The method for dispatching a self-propelled sprayer for reducing soil mechanical compaction and integrating water and fertilizer according to claim 2 or 3, characterized in that: After the position of the i-th bat is updated, it also includes: Find the bat position with the smallest fitness value in the t+1th iteration among all bats, and determine whether the fitness value of the bat position is less than the optimal position of all bats in the tth iteration The fitness value of If yes, the bat position with the smallest fitness value is taken as the optimal position of all bats in the t+1th iteration. Otherwise, the optimal positions of all bats in the tth iteration are As the optimal position of all bats in the t+1th iteration 8. The method for dispatching a self-propelled sprayer for reducing soil mechanical compaction and integrating water and fertilizer according to claim 7, characterized in that: Get the optimal position of all bats in the t+1th iteration After that, determine whether the iteration termination condition is reached. If yes, then output the optimal path, total scheduling cost and total compaction satisfaction after the operation. Otherwise, update the pulse frequency and pulse intensity and proceed to the next iteration.

9. The method for dispatching a self-propelled sprayer for reducing soil mechanical compaction and integrating water and fertilizer according to claim 8, characterized in that: The iteration termination condition is to satisfy any of the following conditions: I. Optimal Location Achieve the specified accuracy of the objective function; II、t+1=T max 。 10. The method for dispatching a self-propelled sprayer for reducing soil mechanical compaction and integrating water and fertilizer according to claim 8, characterized in that: The formula for updating the pulse frequency and pulse intensity is: r i t+1 =r i t ·[1-exp(-γ·t)], In the formula, r i t+1 is the pulse frequency of the i-th bat in the t+1th iteration, is the pulse sound intensity of the i-th bat in the t+1-th iteration, η is the sound intensity attenuation coefficient, and γ is the frequency increase coefficient.

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