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

Abstract

A scheduling method for a self-propelled sprayer for soil mechanical compaction reduction and integrated water and fertilizer management, which belongs to the field of agricultural machinery scheduling. The present invention aims to meet the requirements of the black soil compaction degree of the field block and reduce the scheduling cost. The scheduling method for the self-propelled sprayer for soil mechanical compaction reduction and integrated water and fertilizer management according to the present invention constructs an objective function with the lowest total scheduling cost and the highest compaction satisfaction after operation according to the actual operation requirements of the self-propelled sprayer; uses the objective function as the fitness function of the bat algorithm, and adopts the bat algorithm to schedule the self-propelled sprayer. The present invention is applicable to the field of agricultural machinery scheduling and outputs a scheduling scheme for the self-propelled sprayer.

Description

Technical Field

[0001] The present invention belongs to the field of agricultural machinery scheduling. 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 achieve visualization of the agricultural production process, intelligence of decision-making, precision of operation, and informatization of management. Black soil is a high-quality and scarce cultivated land resource, playing an irreplaceable role in maintaining national food security and the stable development of the economic society. However, due to long-term high-intensity development and utilization and unreasonable tillage methods, the black soil in Northeast China faces serious degradation problems. Although relevant departments have implemented relevant policies and regulations to promote the application of smart agriculture in black soil protection, full coverage of conservation tillage has not been achieved, and the trend of soil erosion has not been fundamentally curbed. The situation of black soil protection remains severe. Reducing the soil compaction degree of black soil can improve soil structure, increase soil fertility, and enhance the soil's water and fertilizer retention capacity. Using a self-propelled sprayer that meets the soil compaction degree for operation can achieve the sustainable use and protection of black soil. Summary of the Invention

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

[0004] The self-propelled sprayer scheduling method for soil mechanical compaction reduction and water and fertilizer integration includes:

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

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

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

[0008] When the random number rand1 is less than the pulse frequency r of the i-th bat at the t-th iteration i t , 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 at the t-th iteration, is the flying speed of the i-th bat at the (t + 1)-th iteration, is the optimal position of all bats at the t-th iteration, ε is a random number in [-1, 1], is the pulse sound intensity of the i-th bat at the t-th iteration, and the random number rand1 ∈ [0, 1];

[0012] When the random number rand2 is less than the pulse sound intensity of the i-th bat at the t-th iteration and the fitness of the estimated position X′ i is less than the fitness of the position the estimated position X′ i is used as the position of the i-th bat at the (t + 1)-th iteration and the position of the i-th bat is updated to the position Otherwise, the i-th bat remains at the position without moving, and the scheduling ends; the random number rand2 ∈ [0, 1].

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

[0014]

[0015] In the formula, ω is the inertia weight;

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

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

[0018]

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

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

[0021] MinZ = αC t - βU,

[0022] where, C t is the total scheduling cost, U is the total compaction satisfaction, Z is the total scheduling objective, and α and β are the coefficients of C t and U respectively.

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

[0024]

[0025] Among them, the path type set P = {1, 2}. When p = 1, it means the path does not pass through the medicine supplement point. When p = 2, it means the path passes through the medicine supplement point;

[0026] The node set V = F ∪ R ∪ {O, D}, where O is the starting point, D is the ending point, F is the set of fields formed by multiple farmlands in the target area, and R is the set of medicine supplement points composed of multiple medicine supplement points;

[0027] is the path decision variable, indicating whether to select the path from node g to node h with type p. If so 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 is the fixed cost of the medicine supplement operation;

[0031] is the operation cost of farmland n;

[0032] Intermediate variable L n is the compaction capacity of the self-propelled sprayer after operating when reaching farmland n, is the compaction satisfaction of farmland n.

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

[0034]

[0035] Among them, indicates whether to select the path from the starting point O to node h with type p. If so otherwise

[0036] indicates whether to select the path from node h to the ending point D with type p. If so 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, and q O is the initial water level of the liquid medicine tank, and are the required amounts of liquid medicine for node g and field n respectively;

[0038] T O is the initial moment, and T h and T g are the moments when reaching node h and node g respectively, is the minimum transfer time from node g to node h under p type, is the minimum transfer time from node h to the end point D under p type, and are the operation times of node g and node h respectively, and t r is the fixed time for the medicine replenishment operation;

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

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

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

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

[0043] Find the position of the bat with the minimum fitness value in all bats at the (t + 1)-th iteration, and determine whether the fitness value of this bat position is less than the fitness value of the optimal position of all bats at the t-th iteration ;

[0044] If so, take the position of the bat with the minimum fitness value as the optimal position of all bats at the (t + 1)-th iteration

[0045] Otherwise, take the optimal position of all bats at the t-th iteration as the optimal position of all bats at the (t + 1)-th iteration

[0046] Furthermore, after obtaining the optimal position of all bats at the (t + 1)-th iteration , determine whether the iteration termination condition is reached,

[0047] Output the optimal path, the total scheduling cost, and the total compaction satisfaction after the operation.

[0048] Otherwise, update the pulse frequency and pulse sound intensity, and perform the next iteration.

[0049] Furthermore, the above iteration termination conditions are satisfied as long as any of the following conditions are met:

[0050] I. The optimal position Reach the specified accuracy of the objective function;

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

[0052] Furthermore, the formulas for updating the pulse frequency and pulse sound intensity are as follows:

[0053]

[0054] In the formula, is the pulse frequency of the i-th bat in the (t + 1)-th 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 scheduling method for a self-propelled sprayer for soil mechanical compaction reduction and water and fertilizer integration, aiming to meet the requirements of the black soil compaction degree of the field and reduce the scheduling cost, and taking into account the situation that the liquid medicine will be used up during the operation of the self-propelled sprayer and it is necessary to go to the medicine replenishment point to replenish the liquid medicine. The present invention establishes a model, the objective function of which 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; subsequently, based on the bat heuristic algorithm, the solution steps of the model are designed, and finally the optimal path and the objective function value are output.

[0056] Through the method of the present invention, a scheduling scheme for multiple self-propelled sprayers can be output based on the two objectives of meeting the soil compaction degree and minimizing the total scheduling cost. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0058] Figure 2 is the flow chart of the bat algorithm. DETAILED DESCRIPTION OF THE INVENTION

[0059] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

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

[0061] Design method:

[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 follows: I. The order of nodes passed by each path; II. Whether each path selects a medicine 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 to evaluate the quality of each bat's position. It includes two objectives: I. Maximize compaction satisfaction: Calculate the satisfaction through the liquid medicine tank water level (converted into compaction degree) when the self-propelled sprayer arrives at the field; II. Minimize the total cost: including path movement cost, operation cost, and medicine replenishment cost.

[0064] 3. Update the speed and position of the bats to generate a new path planning scheme.

[0065] Refer to Figure 1 and Figure 2 To specifically illustrate this embodiment, the self-propelled sprayer scheduling method for soil mechanical compaction reduction and water and fertilizer integration described in this embodiment includes:

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

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

[0068] A set of fields F = {1, 2,..., N} composed of multiple farmlands in the target area;

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

[0070] A set of path types \(P = \{1, 2\}\) composed of multiple path types. When \(p = 1\), it means the path does not pass through the medicine replenishment point. When \(p = 2\), it means the path passes through the medicine replenishment point;

[0071] The set of all nodes \(V=F\cup R\cup\{O, D\}\), where \(O\) is the starting point and \(D\) is the ending point;

[0072] Preset relevant constraint conditions.

[0073] 2. Problem description of the self-propelled sprayer

[0074] This embodiment aims to solve the problem of large-scale self-propelled sprayer operations that need to go to the medicine replenishment point to replenish the liquid medicine midway. Each field has certain requirements for soil compaction. Therefore, the task is to minimize the total scheduling cost and maximize the satisfaction of the field compaction requirements.

[0075] For each field operation point, considering the location factors of the operation point and the medicine replenishment point and the operation matching factors, path arcs are constructed between two nodes. The arcs passing through two nodes are divided into two types:

[0076] (1) Paths that do not pass through the medicine replenishment point; (2) Paths that pass through the medicine replenishment point.

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

[0078] In addition, each field has four indicators: the required liquid medicine volume, the compaction requirement, the operation time, and the operation cost. A maximum operation time is specified for each closed path. The sum of all path times, operation times, and medicine replenishment times on this closed path cannot exceed the maximum operation time, that is, the total operation duration of each medicine replenisher is constrained.

[0079] Step 2: Construct the objective function and the constraint conditions of the objective function according to the problem described in Step 1

[0080] Starting from a garage with multiple self-propelled sprayers having exactly the same parameters as the starting point O, it is necessary to perform spraying operations on multiple fields with different compaction requirements in the area. Each field can only be served once. During the operation, if the remaining capacity of the liquid medicine does not meet the liquid medicine demand of the next field, the self-propelled sprayer needs to go to the medicine replenishment point to replenish the liquid medicine and then carry out the operation. In addition, the self-propelled sprayer needs to return to the garage within the specified maximum operation time. Therefore, the scheduling objectives of the self-propelled sprayer include: (1) The total scheduling cost is the lowest. The total scheduling cost is the sum of the moving cost, the operation cost, and the liquid medicine replenishment cost. (2) The total compaction satisfaction of each field is maximized. Each field has a compaction requirement, and the compaction ability of the self-propelled sprayer after operating in each field is related to the water level of the liquid medicine tank when it arrives at that field.

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

[0082] A. The total scheduling cost objective of the self-propelled sprayer:

[0083]

[0084] Among them, C t is the total scheduling cost; is the path decision variable, indicating whether to select the path from node g to node h and of type p. If so 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 is the fixed cost of the medicine replenishment operation; is the operation cost of field n.

[0085] B. The compaction satisfaction objective after operation. When the compaction degree after operation is less than the compaction requirement of the field, the satisfaction is 1:

[0086]

[0087] Among them, U is the total compaction satisfaction; L n is the compaction ability of the self-propelled sprayer after operating when arriving at field n, the compaction satisfaction of field n.

[0088] C. The total scheduling objective requires the lowest total scheduling cost and the highest total compaction satisfaction after operation

[0089] MinZ = αC t -βU,

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

[0091] 2. Constraint Conditions of the Objective Function

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

[0093]

[0094] Among them, represents whether to select the path from the starting point O to node h and of type p. If so, otherwise N max is the maximum available number of self-propelled sprayers.

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

[0096]

[0097] Among them, represents whether to select the path from node h to the end point D and of type p. If so, otherwise

[0098] (3) The inflow and outflow of each field are equal and can only be accessed once. Then there is a constraint:

[0099]

[0100] (4) The path flowing directly out from the starting point O and the path about to flow directly into the end point will not pass through the medicine replenishment point. Then there is a constraint:

[0101]

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

[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 required amount of liquid medicine at node g, and Q max is the maximum capacity of the liquid medicine tank.

[0105] (6) When reaching a certain field, the water level of 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 of the liquid medicine tank when reaching plot n, and is the required amount of liquid medicine for plot n.

[0108] (7) Restrict the upper and lower limits of the water level of the liquid medicine tank, then there is a constraint:

[0109]

[0110] (8) It is stipulated that the initial water level of the liquid medicine tank is full, then there is a constraint:

[0111] q O = Q max ,

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

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

[0114] T O = 0,

[0115]

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

[0117] (10) The compaction degree after the self-propelled sprayer operates in a certain plot is associated with the water level of the liquid medicine tank when arriving at that plot, then there is a constraint:

[0118]

[0119] Among them, L g is the compaction ability after the self-propelled sprayer operates when reaching node g, and γ is the proportionality coefficient between the compaction degree after the self-propelled sprayer operates and the water level of the liquid medicine tank (when arriving at the plot).

[0120] (11) The value of the binary decision variable, then there is a constraint:

[0121]

[0122] δ p ∈ {0, 1}.

[0123] (12) The value ranges of the field compaction satisfaction, the time point of arriving at the field, and the remaining water level in the liquid medicine tank when arriving at the field, then there are constraints:

[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 the bat position

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

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

[0130] The position update formula is:

[0131]

[0132] Among them, and are the positions of the i-th bat at the (t + 1)-th and t-th iterations respectively, is the flight speed of the i-th bat at the (t + 1)-th iteration, and this flight speed is obtained through the flight speed update formula:

[0133]

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

[0135]

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

[0137] Otherwise, for all bats at the t-th iteration, the optimal positions are randomly perturbed to obtain the position of the i-th bat at the (t + 1)-th iteration Then execute S2.

[0138] The random perturbation formula is:

[0139]

[0140] where ε is a random number in [-1, 1], is the pulse sound intensity of the i-th bat at 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 the fitness value of the position (i.e., the total scheduling objective) is less than the fitness value of the position If so, it means that the position is better than the position Then the i-th bat is updated to the position Otherwise, keep the i-th bat at the original position without moving, and end the scheduling.

[0143] Thus, through the above method, the self-propelled sprayer can find the next target position, that is, the scheduling of the self-propelled sprayer is realized.

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

[0145] S3: Update the optimal position

[0146] Find the position of the bat with the minimum fitness value at the (t + 1)-th iteration among all bats, and determine whether the fitness value of this bat position is better than the fitness value of the position If so, take the position of the bat with the minimum fitness value as the optimal position of all bats at the (t + 1)-th iteration

[0147] Then execute S4; Otherwise, take

[0148] as the optimal position of all bats at the (t + 1)-th iteration Then execute S4. Then execute S4.

[0149] S4: Termination judgment

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

[0151] I. Optimal position Reach the specified accuracy of the objective function.

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

[0153] S5: Output the result

[0154] Output the optimal path, and then calculate the total scheduling cost and the total compaction satisfaction after the operation according to the optimal path.

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

[0156] The update formulas for the new pulse frequency and the pulse sound intensity are as follows:

[0157]

[0158] Where η is the sound intensity attenuation coefficient and γ is the frequency increase coefficient.

[0159] Although the present invention has been 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. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed, as long as they do not deviate from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the different dependent claims and the features described herein can be combined in a manner different from that described in the original claims. It should also be understood that the features described in connection with a single embodiment can 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; The objective function expressions of total compaction satisfaction and total scheduling objectives are as follows: Among them, C t is the total scheduling cost, U is the total compaction satisfaction; 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 = FURU{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 refill 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; 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 available self-propelled sprayers, 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.

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 of the i-th bat in the t-th iteration when the random number rand1 is less than 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 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].

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, Z is the overall scheduling target, α and β are C t and the coefficient of U.

5. The self-propelled sprayer scheduling method for soil mechanical compaction reduction and water-fertilizer integration 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 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: 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.

7. The method for dispatching a self-propelled sprayer for reducing soil mechanical compaction and integrating water and fertilizer according to claim 6, 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 。 8. The method for dispatching a self-propelled sprayer for reducing soil mechanical compaction and integrating water and fertilizer according to claim 6, characterized in that: The formula for updating the pulse frequency and pulse intensity is: 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.

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