A Data-Process-Based Method for Agricultural Water Resource Allocation

Abstract

This invention relates to the field of data processing technology, specifically to a data-process-based method for agricultural water resource allocation. It employs various sensors to collect farmland data at different time points. Based on air temperature and humidity, it obtains the water evaporation potential of each farmland at each time point; based on water depth and soil moisture at each time point, it obtains the crop development water shortage index for each farmland; based on water evaporation potential, crop development water shortage index, and rainfall intensity, it obtains the pheromone volatilization coefficient along the water supply path between farmlands; and based on the pheromone volatilization coefficient, it obtains a real-time optimized water supply path, thus achieving rational water resource allocation. This enables rational water resource allocation, increases the accuracy of the optimized water supply path, and improves the timeliness of farmland irrigation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to an agricultural cultivation water source scheduling method based on data processing. BACKGROUND

[0002] Nowadays, the planting of farmland tends to be large-area intelligent management, among which, the accuracy and efficiency of the irrigation system are necessary conditions for ensuring timely irrigation of crops and normal growth of crops. Different crops planted in different farmlands have different irrigation times, in addition, the environment and weather of farmland will also affect the water supply demand of farmland, and the staff can judge whether the farmland needs water supply irrigation through the collected information data of different farmlands. In order to ensure that crops are irrigated in time and avoid waste of water resources, we need a scientific and reasonable water resource scheduling scheme, and a good real-time farmland water supply optimization path is particularly necessary for the rational scheduling of water resources.

[0003] The ant colony algorithm in the path optimization algorithm has the advantages of strong global optimization ability and good adaptability to different problems, but the pheromone evaporation coefficient in the traditional ant colony algorithm is single, and the next node is inclined to be randomly selected, which although increases the ant colony exploration space, but the positive feedback time also increases, resulting in slow convergence speed.

[0004] In summary, the present application provides an agricultural cultivation water source scheduling method based on data processing, which obtains farmland environment data through various environment data acquisition devices, obtains the evaporation coefficient of each water supply path according to the farmland environment data, calculates the optimized path according to the evaporation coefficient of each water supply path, increases the probability that the water supply route passes through the farmland with high irrigation demand first, and irrigates according to the optimized path, so as to realize the rational scheduling of water resources. SUMMARY

[0005] In order to solve the above technical problems, the present application provides an agricultural cultivation water source scheduling method based on data processing to solve the existing problems.

[0006] The agricultural cultivation water source scheduling method based on data processing of the present application adopts the following technical scheme:

[0007] An embodiment of the present application provides an agricultural cultivation water source scheduling method based on data processing, which comprises the following steps:

[0008] Various sensors are used to collect farmland environment data at each time point;

[0009] The water evaporation potential of each farmland at each time point was obtained based on the ambient temperature and relative humidity of each farmland at each time point; the relative water depth of each farmland at each time point was obtained based on the water depth of each farmland at each time point; the relative soil moisture of each farmland at each time point was obtained based on the soil moisture of each farmland at each time point; and the crop development water shortage index of each farmland at each time point was obtained based on the relative water depth and relative soil moisture of each farmland at each time point.

[0010] The rainfall intensity of each farmland at each time point was obtained; the relative water use comparative advantage of each farmland at each time point was obtained based on the water evaporation potential, crop water shortage index and rainfall intensity.

[0011] The set of neighboring farmlands for each farmland is obtained based on the water supply path of each farmland; the pheromone volatilization coefficient on each water supply path is obtained based on the relative water use comparative advantage of each farmland at each time point and the set of neighboring farmlands for each farmland; the optimized water supply path is obtained based on the pheromone volatilization coefficient on each water supply path; and water resource scheduling is carried out based on the optimized water supply path.

[0012] Preferably, the method of using various sensors to collect farmland environmental data at different time points specifically includes:

[0013] The ambient temperature data of each farmland is obtained by temperature sensors, the relative humidity data of the air and soil moisture data of each farmland are obtained by humidity sensors, and the surface water depth data of each farmland is obtained by water level sensors.

[0014] Preferably, obtaining the water evaporation potential of each farmland at each time point based on the ambient temperature and relative humidity of each farmland at each time point includes: using the product of the relative humidity of each farmland at each time point and the ambient temperature as the water evaporation potential of each farmland at each time point.

[0015] Preferably, obtaining the relative water depth of each farmland at each time point based on the water depth of each farmland at each time point specifically includes: for each farmland where crops require surface water, the ratio of the water depth of the farmland at each time point to the optimal water depth is used as the relative water depth of the farmland at each time point; for each farmland where crops do not require surface water, the relative water depth of the farmland at each time point is obtained through a preset value.

[0016] Preferably, obtaining the relative soil moisture of each farmland at each time point based on the soil moisture of each farmland at each time point specifically includes: taking the ratio of the soil moisture of each farmland to the optimal soil moisture as the relative soil moisture of the farmland at each time point.

[0017] Preferably, the crop development water shortage index for each farmland at each time point is obtained based on the relative water depth and relative soil moisture at each time point, and the expression is as follows:

[0018] For each farmland at each time point, the relative water depth of the farmland is used as the exponent of an exponential function with the natural constant e as the base, and the relative soil moisture of the farmland is used as the exponent of an exponential function with the natural constant e as the base; the reciprocal of the product of the two exponential functions is used as the crop development water shortage index for each farmland at each time point.

[0019] Preferably, the step of obtaining the relative water use comparative advantage of each farmland at each time point based on the water evaporation potential, crop development water shortage index, and rainfall intensity at each time point specifically includes: calculating the sum of the water evaporation potential and the crop development water shortage index of each farmland at each time point, and using the ratio of the sum to the rainfall intensity of each farmland at each time point as the relative water use comparative advantage of each farmland at each time point.

[0020] Preferably, the step of obtaining the set of neighboring farmlands for each farmland based on the water supply path of each farmland specifically involves: for each farmland, counting the farmlands directly connected to the water supply path of the farmland, and taking each farmland directly connected to the water supply path of the farmland as the set of neighboring farmlands.

[0021] Preferably, the step of obtaining the pheromone volatilization coefficient along each water supply path based on the relative water use comparative advantage of each farmland at each time point and the set of neighboring farmlands of each farmland specifically includes:

[0022]

[0023] In the formula, ρ ij (t) represents the volatile matter coefficient along the water supply path between the i-th and j-th farmlands, CAW j (t) represents the comparative advantage of water use for the j-th farmland at time t, allowed. j Let j be the set of neighboring farmlands of the j-th farmland.

[0024] Preferably, the step of obtaining the water supply optimization path based on the pheromone evaporation coefficient of each water supply path specifically involves: using the evaporation coefficient of each water supply path as the pheromone evaporation factor in the ant colony algorithm, inputting it into the probability calculation formula of the ant colony algorithm to obtain the probability of water flowing through each path; and using the ant colony algorithm to calculate the water supply optimization path based on the probability of each water supply path.

[0025] The present invention has at least the following beneficial effects:

[0026] This invention utilizes data processing technology combined with the environmental characteristics of farmland to obtain optimized water supply paths for farmland, and then implements irrigation based on these optimized paths to achieve rational water resource allocation in farmland irrigation. By adaptively acquiring the volatile coefficient of each water supply path, it solves the problem of slow convergence speed caused by a single volatile coefficient in traditional ant colony algorithms, increases the probability that water supply routes will preferentially pass through farmland with higher irrigation demand, increases the accuracy of water supply optimization paths, and thus improves the timeliness of farmland irrigation.

[0027] To address the issue that a single volatile coefficient cannot be combined with the irrigation demand of various farmlands, this invention combines various farmland environmental factors to obtain the relative water use comparative advantage of each farmland at each time point, so as to obtain the volatile coefficient of each water supply path. Based on each volatile coefficient, an optimized water supply path is obtained, and intelligent irrigation of farmland is completed according to the optimized water supply path, which has a high degree of rationality in farmland water resource allocation. Attached Figure Description

[0028] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 A flowchart of the agricultural water source scheduling method based on data processing provided by the present invention;

[0030] Figure 2 A schematic diagram of the water supply route for farmland. Detailed Implementation

[0031] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the data processing-based agricultural water resource scheduling method proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0033] The specific scheme of the agricultural water source scheduling method based on data processing provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0034] An embodiment of the present invention provides a data processing-based method for scheduling agricultural water resources.

[0035] Specifically, the following data processing-based agricultural water resource allocation method is provided; please refer to [link / reference]. Figure 1 The method includes the following steps:

[0036] Step S001: Obtain environmental data for each farmland that requires water supply.

[0037] The system obtains daily water supply data from the local water resources department's monitoring center, identifying the farmland requiring water supply. It also acquires ambient temperature data from temperature sensors, relative humidity and soil moisture data from humidity sensors, and surface water depth data from water level sensors.

[0038] Step S002: Obtain the pheromone volatilization coefficient of each water supply path at each time point based on the environmental data of each farmland at each time point.

[0039] In farmland requiring water supply, different plots have varying water demands. Some farmland has a relatively high and urgent water need and should be prioritized for water supply; others have a relatively low and less urgent need and can have their water supply delayed. However, in traditional ant colony algorithms, because the pheromone evaporation coefficient is the same on all paths, the selection of water supply paths does not take into account the water demand of the farmland. Furthermore, the water demand of each farmland changes over time. Therefore, it is necessary to obtain the pheromone evaporation coefficient of each water supply path at each time point based on the water demand of each farmland, and then update and optimize the paths in real time to ensure that farmland with higher water demand at each time point receives priority water supply. The specific steps are as follows:

[0040] Generally, the higher the ambient temperature and the lower the relative humidity in farmland, the faster the evaporation rate, leading to more severe water loss from crops and soil, and a higher demand for farmland water supply. Conversely, when the temperature is low or the relative humidity is high, evaporation is more difficult and slower, resulting in a relatively lower demand for farmland water supply. Although atmospheric pressure also affects the evaporation rate, the atmospheric pressure in each farmland is relatively stable and within normal atmospheric pressure ranges, so its influence is not considered. Therefore, taking the j-th farmland at time t as an example, the water evaporation potential of each farmland at each time point is:

[0041] E j (t)=β·m j (t)·(T+273)

[0042] In the formula, E j (t) represents the evaporation potential of the j-th farmland at time t, β is the limiting factor, and m j(t) represents the relative humidity of the j-th farmland at time t, and T represents the ambient temperature of the j-th farmland at time t. Here, T is the temperature in Celsius (°C), and T+273 is the corresponding temperature in Kelvin.

[0043] It should be noted that the value of β can be set by the implementer; in this embodiment, the value of β is set to [value to be filled in]. By setting β, the range of water evaporation potential is limited to (0-1) to facilitate subsequent calculations. The higher the water evaporation potential, the faster the water in the farmland evaporates and the higher the farmland's demand for water. The lower the water evaporation potential, the slower the water in the farmland evaporates and the relatively lower the farmland's demand for water.

[0044] Among common crops, some (such as rice) require surface water of a certain depth to grow normally. Taking rice as an example, when the surface water depth is less than the optimal depth for rice growth, the rice's water supply is insufficient, affecting its yield. Therefore, rice has a high water requirement, and the paddy field's water supply demand is high. When the surface water depth exceeds the optimal depth, the paddy field is already saturated and no additional water is needed; therefore, the paddy field's water supply demand is low. Furthermore, each crop has specific soil moisture requirements. When soil moisture is less than the optimal moisture level for crop growth, it affects the crop's water absorption, hindering normal growth and thus impacting yield. Therefore, the farmland's water supply demand is high in this case, and the lower the soil moisture, the drier the land, and the higher the farmland's water supply demand. Conversely, when soil moisture exceeds the optimal moisture level for crop growth, the farmland's water supply demand is low. Therefore, taking the j-th farmland at time t as an example, the crop development water shortage index for each farmland at each time point is obtained based on the water depth and soil moisture at each time point. The expression is as follows:

[0045]

[0046] In the formula, G j (t) represents the crop water shortage index (WHR) for the j-th farmland at time t. j (t) represents the relative water depth of the j-th farmland at time t, WCR j (t) represents the relative soil moisture of the j-th farmland at time t, e () It is an exponential function with base e, and α is a limiting factor; h j (t) represents the water depth of the j-th farmland at time t, h j (0) represents the optimal water depth for the j-th farmland at time t; c j (t) represents the soil moisture of the j-th farmland at time t, and c j(0) represents the optimal soil moisture for the j-th farmland at time t. It should be noted that the value of α can be set by the implementer; in this embodiment, the value of α is set to 1-e. -1 By setting α, the crop water shortage index is limited to the range of (0,1] to facilitate subsequent calculations; when the j-th farmland requires surface water at time t, WHR j The smaller the value of WHR, the greater the water demand of the farmland. Therefore, this farmland should be prioritized for water supply. j The larger the value of (t), the smaller the water demand of the farmland, and the water supply to the farmland can be appropriately delayed. When the j-th farmland does not need surface water at time t, WHR will be... j The value of (t) is set to a constant -1, so that when calculating the crop development water shortage index, The result is 1, thus avoiding the calculation of the crop water shortage index due to the lack of water depth parameters in the farmland. WCR j The smaller the value of (t), the drier the soil, and the greater the water demand of the j-th farmland at time t; WCR j The larger the value of (t), the smaller the water requirement of the farmland. j (t) represents the crop water shortage index for the j-th farmland at time t, G j The larger the value of (t), the greater the water demand of the farmland, and the higher the priority of water supply to the farmland should be; G j The smaller the value of (t), the less water the farmland needs, and the lower the priority of water supply to the farmland should be.

[0047] The rainfall intensity of each farmland at each time point was obtained from the rainfall observation system. The specific method is a well-known technique and will not be elaborated here. Since rainfall provides additional water supply to farmland, taking the j-th farmland at time point t as an example, the relative water use comparative advantage of each farmland at each time point is calculated based on the above two indicators combined with the rainfall intensity:

[0048]

[0049] In the formula, CAW j (t) represents the comparative advantage of water use for the j-th farmland at time t, E j (t) represents the evaporation potential of the j-th farmland at time t, G j (t) represents the crop water shortage index for the j-th farmland at time t, R j (t) represents the rainfall intensity of the j-th farmland at time t, exp() is an exponential function with base e, and γ is a constant to prevent the denominator from being 0. The implementer can choose the value of γ as they see fit. In this embodiment, the value of γ is set to 1.

[0050] When there is rainfall in the area where the j-th farmland is located at time t, the amount of water that needs to be delivered by the water supply center will decrease, so the water supply sequence for this farmland can be appropriately delayed; when there is no rainfall in the farmland at time t, all the farmland's water needs come from the water supply center, and the farmland's water demand remains unchanged, so the water supply sequence cannot be delayed.

[0051] The higher the relative water use comparative advantage of a farmland, the higher its water supply priority. The water supply path should preferentially pass through this farmland, meaning the pheromone evaporation coefficient on the water supply path from other farmlands to this farmland should be lower, resulting in more pheromone retention on the path and increasing the probability of ants choosing this farmland. Conversely, the lower the relative water use comparative advantage of a farmland, the more appropriate it is to postpone water supply. This means the pheromone evaporation coefficient on the path from other farmlands to this farmland should be higher, resulting in less pheromone retention on the path and decreasing the probability of ants choosing this farmland.

[0052] Taking the j-th farmland as an example, such as Figure 2 As shown, the large circle represents the water supply center, the small circles represent the farmland, and the straight lines represent the water supply paths in the irrigation system. Since not every two farmlands have a directly connected water supply path, farmlands 5, 7, 8, and 9 are directly connected to farmland j. Therefore, the set of other farmlands directly connected to farmland j through water supply paths is allowed. j ={5,7,8,9}, will allow the set j This is the set of neighboring farmlands of the j-th farmland. The pheromone evaporation coefficient along each water supply path is then constructed, expressed as:

[0053]

[0054] In the formula, ρ ij (t) represents the volatile matter coefficient along the water supply path between the i-th and j-th farmlands, CAW j (t) represents the comparative advantage of water use for the j-th farmland at time t, allowed. j Let j be the set of neighboring farmlands of the j-th farmland.

[0055] The greater the comparative advantage of water use for the j-th farmland, the greater the demand for water in the region. Therefore, there should be relatively more pheromones on the path from other farmlands to the j-th farmland, meaning the pheromone volatility coefficient on the water supply path connected to the j-th farmland is smaller. Conversely, the smaller the comparative advantage of water use for the j-th farmland, the less the demand for water in the region. Therefore, there should be relatively less retained pheromones on the path from other farmlands to the j-th farmland, meaning the pheromone volatility coefficient on the water supply path connected to the j-th farmland is larger.

[0056] Step S003: Obtain the real-time optimized path for farmland water supply through the ant colony algorithm, and send the real-time optimized path to the water supply center to complete the rational allocation of water resources.

[0057] The above steps determined the volatile coefficients along each water supply path. These coefficients were then used as pheromone volatile factors in the ant colony algorithm, inputting them into the algorithm's probability calculation formula. This process calculated the probability of water flowing through each path from the water supply center. The probability calculation is a well-known technique and will not be elaborated upon here. Substituting the probabilities of each water supply path into the ant colony algorithm for modeling, an optimized path for supplying water to each farmland was obtained. The local agricultural and water conservancy monitoring center sent this optimized farmland water supply path to the water supply center, which then supplied water to each farmland according to the optimized path, ensuring the normal operation of agricultural production.

[0058] In summary, this invention, through data processing technology combined with the environmental characteristics of farmland, obtains optimized water supply paths for farmland and implements rational water resource allocation during irrigation based on these optimized paths. By adaptively acquiring the volatile coefficient of each water supply path, it solves the problem of slow convergence speed caused by a single volatile coefficient in traditional ant colony algorithms, increases the probability that water supply routes will preferentially pass through farmland with higher irrigation demand, increases the accuracy of optimized water supply paths, and thus improves the timeliness of farmland irrigation.

[0059] To address the issue that a single volatile coefficient cannot be combined with the irrigation demand of each farmland, this embodiment combines environmental factors of each farmland to obtain the relative water use comparative advantage of each farmland at each time point, so as to obtain the volatile coefficient of each water supply path. Based on each volatile coefficient, an optimized water supply path is obtained, and intelligent irrigation of farmland is completed according to the optimized water supply path, which has a high degree of rationality in farmland water resource allocation.

[0060] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0061] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0062] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An agricultural cultivation water source scheduling method based on data processing, characterized by, The method comprises the following steps: Various types of sensors are used to collect farmland environment data at each time point; According to the environmental temperature and air relative humidity of each farmland at each time point, the water evaporation potential of each farmland at each time point is obtained; according to the water depth of each farmland at each time point, the relative water depth of each farmland at each time point is obtained; according to the soil humidity of each farmland at each time point, the relative soil humidity of each farmland at each time point is obtained; according to the relative water depth and relative soil humidity of each farmland at each time point, the crop development water deficiency index of each farmland at each time point is obtained; The rainfall intensity of each farmland at each time point is obtained; according to the water evaporation potential, crop development water deficiency index and rainfall intensity of each farmland at each time point, the relative water use comparative advantage degree of each farmland at each time point is obtained; According to the water supply path of each farmland, the near neighbor farmland set of each farmland is obtained; according to the relative water use comparative advantage degree of each farmland at each time point and the near neighbor farmland set of each farmland, the pheromone volatilization coefficient on each water supply path is obtained, specifically including: In the formula, is the relative water supply advantage of the first farmland at the time point, is the relative water supply advantage of the second farmland at the time point, is a volatility coefficient on a water supply path between the first farmland and the second farmland, is is the relative water supply advantage of the first farmland at the time point, is the relative water supply advantage of the second farmland at the time point, is a near neighbor farmland set of the first farmland, is a near neighbor farmland set of the second farmland; According to the pheromone volatilization coefficient on each water supply path, the water supply optimization path is obtained, specifically: taking the volatilization coefficient on each water supply path as the pheromone volatilization factor in the ant colony algorithm, inputting into the probability calculation formula of the ant colony algorithm to obtain the probability of water flow passing through each path; according to the probability of each water supply path, the water supply optimization path is calculated by using the ant colony algorithm; According to the water supply optimization path, water source scheduling is performed.

2. The data processing-based agricultural cultivation water source scheduling method according to claim 1, characterized by, The various types of sensors are used to collect farmland environment data at each time point, specifically: The temperature sensor is used to obtain the environmental temperature data of each farmland, the humidity sensor is used to obtain the air relative humidity data and soil humidity data of each farmland, and the water level sensor is used to obtain the land surface water depth data of each farmland.

3. The data processing-based agricultural cultivation water source scheduling method according to claim 1, characterized by, The water evaporation potential of each farmland at each time point is obtained according to the environmental temperature and air relative humidity of each farmland at each time point, including: taking the product of the air relative humidity and the environmental temperature of each farmland at each time point as the water evaporation potential of each farmland at each time point.

4. The data processing-based agricultural cultivation water source scheduling method according to claim 1, characterized by, The relative water depth of each farmland at each time point is obtained according to the water depth of each farmland at each time point, specifically including: for each farmland that requires surface water for crop growth, taking the ratio of the water depth of the farmland at each time point to the optimal water depth as the relative water depth of the farmland at each time point; for each farmland that does not require surface water for crop growth, the relative water depth of the farmland at each time point is obtained by a preset value.

5. The data processing based agricultural cultivation water source scheduling method of claim 1, wherein, The relative soil humidity of each farmland at each time point is obtained according to the soil humidity of each farmland at each time point, specifically including: taking the ratio of the soil humidity of each farmland to the optimal soil humidity as the relative soil humidity of the farmland at each time point.

6. The data processing based agricultural cultivation water source scheduling method of claim 1, wherein, The crop development water deficiency index of each farmland at each time point is obtained according to the relative water depth and relative soil humidity of each farmland at each time point, including: For each field at each time point, the relative waterlogging depth of the field is taken as the exponent of an exponential function with a natural constant For each field at each time point, the relative soil moisture of the field is taken as the exponent of an exponential function with a natural constant The inverse of the product of the two exponential functions is taken as the crop development water deficit index for each field at each time point; The relative water depth is specifically: The relative soil humidity is specifically: The crop development water deficiency index is specifically: In the formula, for Time point Water shortage index for crop development in individual farmlands for Time point The relative water depth of each farmland for Time point The relative soil moisture of each farmland For An exponential function with base 0. As a limiting factor; for Time point The depth of water accumulation in each farmland, for Time point The optimal water depth for each type of farmland; for Time point Soil moisture in each farmland for Time point The optimal soil moisture for each farmland.

7. The data processing based agricultural cultivation water source scheduling method of claim 1, wherein, The relative water use comparative advantage degree of each farmland at each time point is obtained according to the water evaporation potential, the crop development water deficit index and the rainfall intensity of each farmland at each time point, and specifically includes: calculating the sum value of the water evaporation potential and the crop development water deficit index of each farmland at each time point, and taking the ratio of the sum value to the rainfall intensity of each farmland at each time point as the relative water use comparative advantage degree of each farmland at each time point.

8. The data processing based agricultural cultivation water source scheduling method of claim 1, wherein, The set of neighboring farmlands of each farmland is obtained according to the water supply path of each farmland, and specifically includes: for each farmland, the farmlands directly connected with the water supply path of the farmland are counted, and each farmland directly connected with the water supply path of the farmland is taken as the set of neighboring farmlands.

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