CN114372611A - Water resource optimal allocation method and device - Google Patents
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Abstract
The invention provides a water resource optimal allocation method and a device, wherein humidity information near crop soil in a farmland, which is acquired by a plurality of sensors at preset sampling time, is acquired; acquiring temperature information of a farmland at preset sampling time; determining the growth period of the crops according to the types and the seasonal time of the crops, and determining the transpiration amount of the crops according to the growth period of the crops; grouping at least one sensor according to the humidity information, the temperature information and the crop transpiration amount to obtain a plurality of sensor groups and an average crop bottom soil moisture index corresponding to each sensor group; inputting the average crop bottom soil moisture index and the crop growth period into an irrigation quantity determination neural network model to obtain the irrigation quantity of a farmland area corresponding to the sensor group; the irrigation quantity determination neural network model is obtained by training based on an average crop bottom soil moisture index sample, a crop growth period sample and a predetermined irrigation quantity. The method realizes the accurate irrigation of the farmland and saves water.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a water resource optimal allocation method and device.
Background
In the existing agricultural development, irrigation modes for large-scale farmlands include drip irrigation, sprinkling irrigation, flood irrigation and the like. In China, the farmland area is large, and the conventional irrigation mode mainly adopts flood irrigation, namely, no trench and peduncle are made between the farmlands, and the farmland is allowed to flood on the ground during irrigation, so that the soil is infiltrated under the action of gravity.
The flood irrigation method is a relatively extensive irrigation method, and water resource waste is easily caused by large-area irrigation. In the prior art, the humidity change of a farmland can be monitored through a sensor, so that the irrigation of water resources is controlled, but the difference of water quantity required by crops in each growth period and the difference of water evaporation quantity in each growth period are not considered, and the accurate irrigation of the water resources is difficult to realize.
Disclosure of Invention
The invention provides a water resource optimal allocation method and a water resource optimal allocation device, which are used for solving the defect that the irrigation of water resources is difficult to realize accurate control in the prior art and realizing the irrigation of the water resources.
In a first aspect, the present invention provides a water resource optimization allocation method, including:
acquiring humidity information near crop soil in a farmland, which is acquired by a plurality of sensors at preset sampling time;
acquiring temperature information of the farmland at the preset sampling time;
determining the growth period of the crops according to the types and seasonal time of the crops, and determining the crop transpiration amount according to the growth period of the crops;
grouping the sensors according to the humidity information, the temperature information and the crop transpiration amount to obtain at least one sensor group and an average crop bottom soil moisture index corresponding to each sensor group;
inputting the average crop bottom soil moisture index and the crop growth period into an irrigation quantity determination neural network model to obtain the irrigation quantity of a farmland area corresponding to the sensor group;
the irrigation quantity determination neural network model is obtained by training based on an average crop bottom soil moisture index sample, a crop growth period sample and a predetermined irrigation quantity.
Further, according to the water resource optimal allocation method provided by the present invention, the grouping the plurality of sensors according to the humidity information, the temperature information, and the crop evapotranspiration amount to obtain at least one sensor group specifically includes:
determining an initial crop bottom soil moisture index corresponding to each sensor according to the humidity information, the temperature information and the crop evapotranspiration amount;
and grouping the sensors according to the initial crop bottom soil moisture index and the humidity information to obtain at least one sensor group.
Further, according to the water resource optimal allocation method provided by the present invention, the obtaining of the average crop bottom soil moisture index corresponding to each sensor group according to the humidity information, the temperature information and the crop evapotranspiration amount of each sensor in each sensor group specifically includes:
determining a humidity sequence corresponding to each sensor in each sensor group according to the humidity information;
determining the difference distance between any two sensors in the sensor group according to the humidity sequence;
determining a data unification coefficient of the sensor group according to the difference distance;
adjusting the initial crop bottom soil moisture index corresponding to each sensor according to the data unification coefficient to obtain an adjusted crop bottom soil moisture index;
and adding according to the adjusted crop bottom soil moisture indexes to obtain the average crop bottom soil moisture index corresponding to each sensor group.
Further, according to the water resource optimal allocation method provided by the present invention, the grouping the plurality of sensors according to the initial crop bottom soil moisture indicator and the humidity information to obtain at least one sensor group specifically includes:
determining the grouping distance of every two sensors according to the initial crop bottom soil moisture index and the humidity sequence corresponding to the sensors;
and clustering the sensors by using a density clustering algorithm according to the grouping distance to obtain at least one clustering cluster as a corresponding sensor group.
Further, according to the water resource optimization allocation method provided by the present invention, the clustering the sensors according to the grouping distance by using a density clustering algorithm to obtain at least one cluster as a corresponding sensor group includes:
if the number of the sensors in the sensor group is one, the sensors are abnormal sensors;
obtaining an index difference average value of the initial crop bottom soil moisture indexes of the abnormal sensor and other sensors in a preset neighborhood range;
comparing the index difference average value with a preset difference threshold value, replacing the initial crop bottom soil moisture index of the abnormal sensor with the initial crop bottom soil moisture index average value in the preset neighborhood range if the index difference average value is larger than the preset difference threshold value, and clustering and grouping the abnormal sensors again based on the initial crop bottom soil moisture index average value;
and if the index difference average value is not greater than the preset difference threshold value, taking the abnormal sensor as an independent sensor group without clustering again.
Further, according to the water resource optimal allocation method provided by the invention, a first model is adopted to determine the grouping distance of two sensors according to the initial crop bottom soil moisture index and the humidity sequence corresponding to the sensors, wherein the first model comprises:
wherein D is the grouping distance, WAIs the initial crop bottom soil moisture indicator of sensor A, WBIs the initial crop bottom soil moisture indicator of sensor B, SASaid humidity sequence, S, for sensor ABIs a sensorB, PPMCC () is a pearson coefficient calculation function.
In a second aspect, the present invention provides a water resource optimizing and distributing apparatus, including:
the humidity information acquisition module is used for acquiring humidity information, collected by the sensors at preset sampling time, of the vicinity of crop soil in the farmland;
the temperature information acquisition module is used for acquiring the temperature information of the farmland at the preset sampling time;
the transpiration amount acquisition module is used for determining the growth period of the crops according to the types and seasonal time of the crops and determining the transpiration amount of the crops according to the growth period of the crops;
the index determining module is used for grouping the sensors according to the humidity information, the temperature information and the crop transpiration amount to obtain at least one sensor group and an average crop bottom soil moisture index corresponding to each sensor group;
the irrigation quantity determining module is used for inputting the average crop bottom soil moisture index and the crop growth period into an irrigation quantity determining neural network model to obtain the irrigation quantity of a farmland area corresponding to the sensor group;
the irrigation quantity determination neural network model is obtained by training based on an average crop bottom soil moisture index sample, a crop growth period sample and a predetermined irrigation quantity.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of any one of the above water resource optimization allocation methods.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any of the above-described methods for optimized allocation of water resources.
The invention also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of any of the above-described methods for optimized allocation of water resources.
According to the method and the device for optimizing and distributing the water resources, humidity information near crop soil in a farmland, which is acquired by a plurality of sensors at preset sampling time, is acquired; acquiring temperature information of the farmland at the preset sampling time; determining the growth period of the crops according to the types and seasonal time of the crops, and determining the crop transpiration amount according to the growth period of the crops; grouping the sensors according to the humidity information, the temperature information and the crop transpiration amount to obtain at least one sensor group and an average crop bottom soil moisture index corresponding to each sensor group; inputting the average crop bottom soil moisture index and the crop growth period into an irrigation quantity determination neural network model to obtain the irrigation quantity of a farmland area corresponding to the sensor group; the irrigation quantity determination neural network model is obtained by training based on an average crop bottom soil moisture index sample, a crop growth period sample and a predetermined irrigation quantity. The method realizes the accurate irrigation of the farmland and saves water.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a water resource optimization allocation method provided by the present invention;
FIG. 2 is a schematic structural diagram of a water resource optimizing and distributing device provided by the present invention;
fig. 3 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The following describes a water resource optimal allocation method according to the present invention with reference to fig. 1, which includes:
step 100: acquiring humidity information near crop soil in a farmland, which is acquired by a plurality of sensors at preset sampling time;
specifically, in order to enable the humidity sensor to accurately acquire the humidity information of the whole farmland, the sensors are equidistantly distributed in the farmland at a preset fixed distance, namely, the sensors are deployed in a matrix manner. And the preset height of the sensor needs to be appropriate, so that the humidity information of the farmland can be accurately acquired. In the embodiment of the invention, the preset height is set to be 25 cm, the fixed distance is set to be 100 m, the number of the sensors can be set according to the scale of the farmland, and the number of the sensors is not limited. The humidity sensor will be collectively referred to as a sensor in the subsequent description.
Step 200: acquiring temperature information of the farmland at the preset sampling time;
specifically, the environmental temperature information of the farmland can be acquired according to the temperature sensor or the environmental temperature of the same day, which is not limited herein, and it should be noted that the sampling time of the environmental temperature information is the same as the sampling time of the humidity information. In the embodiment of the invention, the sampling time is set to be 1 hour for sampling once, the length of the humidity sequence is 24, and the data information of 24 time points in one day is obtained.
Step 300: determining the growth period of the crops according to the types and seasonal time of the crops, and determining the crop transpiration amount according to the growth period of the crops;
specifically, because the crop transpiration amount of different growth periods of different crops is different, for example, the growth periods of wheat include a seedling stage, an overwintering period, a jointing stage, a heading stage and a filling stage, the wheat in the several growth periods have different growth processes, the generated crop transpiration amount is also different, the more the water demand of the crops is in the later growth period, the larger the crop transpiration amount is, and therefore the crop transpiration amount corresponding to the crop growth period of the crops in the current season time in the farmland needs to be considered when the condition that the soil needs to be irrigated is considered.
Step 400: grouping the sensors according to the humidity information, the temperature information and the crop transpiration amount to obtain at least one sensor group and an average crop bottom soil moisture index corresponding to each sensor group;
specifically, the amount of water vapor contained in the air is different due to different temperatures, namely the higher the temperature is, the larger the water evaporation amount is, and the smaller the bottom soil moisture is; the lower the temperature, the smaller the water evaporation amount and the larger the bottom soil moisture. Meanwhile, because the data of one sensor represents the information of a small-range area near the sensor, in order to facilitate the subsequent operation of the irrigation system, the sensors need to be grouped according to the difference of initial crop bottom soil moisture between the sensors and the similarity of humidity information, so as to obtain a plurality of sensor groups, and each sensor group represents a sensor set in a large area of a similar soil environment. Thus, the plurality of sensors are grouped according to the acquired humidity information, temperature information and crop transpiration amount. And determining the average crop bottom soil moisture index corresponding to each group of sensors.
Step 500: inputting the average crop bottom soil moisture index and the crop growth period into an irrigation quantity determination neural network model to obtain the irrigation quantity of a farmland area corresponding to the sensor group;
the irrigation quantity determination neural network model is obtained by training based on an average crop bottom soil moisture index sample, a crop growth period sample and a predetermined irrigation quantity.
Specifically, in the embodiment of the present invention, the training method of the irrigation quantity determination neural network model includes: and obtaining the average crop bottom soil moisture indexes of a plurality of areas and the required irrigation quantity corresponding to the areas in the corresponding growth period according to historical data. The average crop bottom soil moisture index and the growing period are used as training data, and the required irrigation quantity is used as label data. Inputting the training data and the label data into a deep neural network for training, and performing iterative updating on the deep neural network by using a cross entropy loss function. It should be noted that the nature of the neural network model for determining irrigation quantity is a classification task, and therefore, the neural network model can be implemented by using neural networks with various structures, which is not limited herein. Therefore, the average crop bottom soil moisture index and the growth period of the crops are input into the trained irrigation quantity determination neural network model, and the corresponding irrigation quantity can be obtained.
According to the water resource optimization allocation method provided by the invention, humidity information near crop soil in a farmland, which is acquired by a plurality of sensors at preset sampling time, is acquired; acquiring temperature information of the farmland at the preset sampling time; determining the growth period of the crops according to the types and seasonal time of the crops, and determining the crop transpiration amount according to the growth period of the crops; grouping the sensors according to the humidity information, the temperature information and the crop transpiration amount to obtain a plurality of sensor groups and an average crop bottom soil moisture index corresponding to each sensor group; inputting the average crop bottom soil moisture index and the crop growth period into an irrigation quantity determination neural network model to obtain the irrigation quantity of a farmland area corresponding to the sensor group; the irrigation quantity determination neural network model is obtained by training based on an average crop bottom soil moisture index, the growing period of crops and a predetermined irrigation quantity. The method realizes the accurate irrigation of the farmland and saves water. The embodiment of the invention combines the humidity information of the crop soil, the environmental temperature and the crop transpiration amount in the current growth period to obtain the crop bottom soil moisture index. And expressing the humidity characteristic of the current soil according to the crop bottom soil moisture index. Whether the current position needs irrigation can be judged according to the index of the soil moisture of the crop bottom. And further, grouping according to the data of the sensors to obtain a plurality of sensor groups. The analysis is carried out aiming at each sensor group, so that the farmland is divided into a plurality of regions, the environment in the regions is the same, the accuracy of subsequent irrigation is guaranteed, and water resources are saved better according to the region analysis irrigation condition.
According to the embodiment of the invention, the sensor data in the same environment area should have similarity, if the difference exists, the data in the sensor group is not uniform, and abnormal data caused by abnormal environment or other special reasons may exist. Therefore, the initial crop bottom soil moisture index is adjusted according to the data unification degree of the sensor group, the total crop bottom soil moisture of the sensor group is obtained after accumulation, the reference of the adjusted total crop bottom soil moisture is stronger, and whether irrigation is needed in the current area or not and the needed irrigation quantity can be judged according to the total crop bottom soil moisture.
Further, according to the water resource optimal allocation method provided by the present invention, the grouping the plurality of sensors according to the humidity information, the temperature information, and the crop evapotranspiration amount to obtain at least one sensor group specifically includes:
determining an initial crop bottom soil moisture index corresponding to each sensor according to the humidity information, the temperature information and the crop evapotranspiration amount;
and grouping the sensors according to the initial crop bottom soil moisture index and the humidity information to obtain a plurality of sensor groups.
Specifically, the humidity information and the crop transpiration amount obtain an initial crop bottom soil moisture index, and the method specifically comprises the following steps: and obtaining an initial crop bottom soil moisture index according to an initial crop bottom soil moisture index formula. The initial crop bottom soil moisture index formula comprises:
wherein W is the initial crop bottom soil moisture index, siHumidity information at the i-th moment, tiThe environmental temperature at the ith moment, n is the length of the humidity sequence, and Q is the corresponding crop evapotranspiration amount. It should be noted that, in the examples of the present invention, the initial crop bottom soil moisture index is an index amount, which means the degree of soil moisture, and is not a specific physical amount.
Because the data from one sensor represents information about a small area near the sensor, to facilitate subsequent operation of the irrigation system, the sensors need to be grouped according to differences in initial crop moisture and similarity of moisture information between the sensors to obtain multiple sensor groups, each sensor group representing a collection of sensors in a large area of a similar soil environment.
Further, according to the water resource optimal allocation method provided by the present invention, the obtaining of the average crop bottom soil moisture index corresponding to each sensor group according to the humidity information, the temperature information and the crop evapotranspiration amount of each sensor in each sensor group specifically includes:
determining a humidity sequence corresponding to each sensor in each sensor group according to the humidity information;
determining the difference distance between any two sensors in the sensor group according to the humidity sequence;
determining a data unification coefficient of the sensor group according to the difference distance;
adjusting the initial crop bottom soil moisture index corresponding to each sensor according to the data unification coefficient to obtain an adjusted crop bottom soil moisture index;
and adding according to the adjusted crop bottom soil moisture indexes to obtain the average crop bottom soil moisture index corresponding to each sensor group.
In particular, because the sensor group represents an area where the environmental characteristics are similar, the sensor data in that area should be similar and uniform. If fluctuation data exists in the area, the data uniformity of the area is influenced, and the initial crop bottom soil moisture index referential performance in the area is insufficient, so that irrigation analysis cannot be performed on the area. Therefore, the data uniformity degree in each sensor group needs to be analyzed, which specifically includes:
and taking the Pearson distance of the humidity sequence between the sensors as a difference distance to obtain the difference distance of the humidity sequence between each sensor in the sensor group. The larger the difference distance is, the more scattered the data is, so the reciprocal of the sum of all the difference distances in the sensor group is taken as the data unification degree. The larger the data unification degree is, the more consistent the data in the sensor group is, and the stronger the initial soil moisture content referential property in the sensor group is.
Taking the data unification degree as a weight, multiplying the weight by all initial crop bottom soil moisture indexes in the sensor group, and adjusting the initial crop bottom soil moisture indexes to obtain crop bottom soil moisture indexes, wherein the larger the data unification degree in the sensor group is, the more the initial crop bottom soil moisture indexes in the sensor group do not need to be adjusted; the smaller the data unification degree in the sensor group is, the smaller the initial crop bottom soil moisture index reference in the sensor group is, the smaller the data needs to be adjusted, and the data error of a single sensor is reduced.
Accumulating the indexes of the soil moisture of the bottoms of all the crops in the sensor group to obtain the index of the soil moisture of the bottoms of the average crops. And taking the average crop bottom soil moisture index as a reference index of the area.
Further, according to the water resource optimal allocation method provided by the present invention, the grouping the plurality of sensors according to the initial crop bottom soil moisture indicator and the humidity information to obtain at least one sensor group specifically includes:
determining the grouping distance of every two sensors according to the initial crop bottom soil moisture index and the humidity sequence corresponding to the sensors;
and clustering the sensors by using a density clustering algorithm according to the grouping distance to obtain at least one clustering cluster as a corresponding sensor group.
Specifically, the grouping distance is obtained according to a grouping distance formula. The grouping distance formula includes:
wherein D is the grouping distance, WAIs an initial crop bottom soil moisture indicator of sensor A, WBIs an initial crop bottom soil moisture indicator of sensor B, SAIs the humidity sequence of sensor A, SBFor the humidity sequence of sensor B, PPMCC () is a pearson coefficient calculation function. In the grouping distance formula, the Pearson coefficient in the denominator represents the variation trend of the two sequences, the greater the Pearson coefficient is, the more similar the variation trends of the two sequences are, and the two sensorsThe smaller the grouping distance of (a); the smaller the difference of initial crop bottom soil moisture indexes in the molecules is, the more similar the data collected by the two sensors is, and the smaller the grouping distance between the two sensors is.
And grouping the sensors by using a density clustering algorithm according to the grouping distance to obtain a plurality of clustering clusters. Each cluster is a sensor group.
Further, according to the water resource optimization allocation method provided by the present invention, the clustering the sensors according to the grouping distance by using a density clustering algorithm to obtain at least one cluster as a corresponding sensor group includes:
if the number of the sensors in the sensor group is one, the sensors are abnormal sensors;
obtaining an index difference average value of the initial crop bottom soil moisture indexes of the abnormal sensor and other sensors in a preset neighborhood range;
comparing the index difference average value with a preset difference threshold value, replacing the initial crop bottom soil moisture index of the abnormal sensor with the initial crop bottom soil moisture index average value in the preset neighborhood range if the index difference average value is larger than the preset difference threshold value, and clustering and grouping the abnormal sensors again based on the initial crop bottom soil moisture index average value;
and if the index difference average value is not greater than the preset difference threshold value, taking the abnormal sensor as an independent sensor group without clustering again.
Specifically, in the clustering result, it is possible to generate a plurality of isolated samples that do not belong to any one cluster. The formation of isolated samples may be due to a particular environmental zone or sensor data anomaly, taking into account that the crop growth environment of adjacent zones is substantially consistent, i.e. the sensor data should not differ too much. Therefore, the analysis of the isolated sample and other adjacent sensors specifically includes:
and taking the isolated sample in the clustering result as an abnormal sensor. And obtaining the index difference average value of the initial crop bottom soil moisture indexes of the abnormal sensor and other sensors in the preset neighborhood range. If the index difference average value is larger than a preset difference threshold value, setting the initial crop bottom soil moisture index of the corresponding abnormal sensor as the initial crop bottom soil moisture index average value in the neighborhood range and clustering and grouping the abnormal sensors again; otherwise, the abnormal sensor is used as a sensor group which only comprises one sensor.
In the embodiment of the present invention, the neighborhood range is set to be 8 neighborhood ranges. The difference threshold is set to 0.2, namely when the index difference average value is larger than 0.2, the data of the abnormal sensor is considered to be unreliable, and value assignment is needed. Reclustering and grouping abnormal sensors comprises the following steps: and judging which sensor group the abnormal sensor belongs to according to the grouping distance between the abnormal sensor after reassignment and the clustering centers of other sensor groups.
Further, according to the water resource optimal allocation method provided by the invention, a first model is adopted to determine the grouping distance of every two sensors according to the initial crop bottom soil moisture index and the humidity sequence corresponding to the sensors, wherein the first model comprises:
wherein D is the grouping distance, WAIs the initial crop bottom soil moisture indicator of sensor A, WBIs the initial crop bottom soil moisture indicator of sensor B, SASaid humidity sequence, S, for sensor ABFor the humidity sequence of sensor B, PPMCC () is a pearson coefficient calculation function.
Referring to fig. 2, the present invention provides a water resource optimizing and distributing apparatus, including:
the humidity information acquisition module 21 is configured to acquire humidity information near crop soil in a farmland, which is acquired by a plurality of sensors at preset sampling time;
the temperature information acquisition module 22 is used for acquiring the temperature information of the farmland at the preset sampling time;
an amount-of-transpiration obtaining module 23, configured to determine a growth period of the crop according to the type of the crop and the seasonal time, and determine an amount of crop transpiration according to the growth period of the crop;
an index determining module 24, configured to group the multiple sensors according to the humidity information, the temperature information, and the crop evapotranspiration amount to obtain at least one sensor group and an average crop bottom soil moisture index corresponding to each sensor group;
the irrigation quantity determining module 25 is configured to input the average crop bottom soil moisture indicator and the growth period of the crop into an irrigation quantity determining neural network model to obtain an irrigation quantity of a farmland area corresponding to the sensor group;
the irrigation quantity determination neural network model is obtained by training based on an average crop bottom soil moisture index sample, a crop growth period sample and a predetermined irrigation quantity.
Since the apparatus provided by the embodiment of the present invention can be used for executing the method described in the above embodiment, and the operation principle and the beneficial effect are similar, detailed descriptions are omitted here, and specific contents can be referred to the description of the above embodiment.
According to the water resource optimization distribution device provided by the embodiment of the invention, the initial crop bottom soil moisture index is obtained according to the humidity information, the environmental temperature and the crop transpiration amount of crops in a farmland. And grouping the sensors according to the corresponding initial crop bottom soil moisture index difference and the similarity of the humidity information among the sensors to obtain a plurality of sensor groups. And acquiring the data unification degree of the sensor group according to the difference of the humidity sequences in the sensor group, and adjusting the initial crop bottom soil moisture index in the corresponding sensor group according to the data unification degree to acquire the crop bottom soil moisture index. And (4) sending the growing period of the crops and the average crop bottom soil moisture index in the sensor group into a deep neural network for analysis, and outputting the required irrigation quantity. The embodiment of the invention realizes the optimized allocation of water resources by grouping and analyzing the sensor data and by data with strong referential property.
Further, according to the water resource optimization allocation apparatus provided by the present invention, the index determining module 24 is specifically configured to:
determining an initial crop bottom soil moisture index corresponding to each sensor according to the humidity information, the temperature information and the crop evapotranspiration amount;
and grouping the sensors according to the initial crop bottom soil moisture index and the humidity information to obtain at least one sensor group.
Further, according to the water resource optimization allocation apparatus provided by the present invention, the index determining module 24 is specifically configured to:
determining a humidity sequence corresponding to each sensor in each sensor group according to the humidity information;
determining the difference distance between any two sensors in the sensor group according to the humidity sequence;
determining a data unification coefficient of the sensor group according to the difference distance;
adjusting the initial crop bottom soil moisture index corresponding to each sensor according to the data unification coefficient to obtain an adjusted crop bottom soil moisture index;
and adding according to the adjusted crop bottom soil moisture indexes to obtain an average crop bottom soil moisture index corresponding to each sensor group.
Further, according to the water resource optimization allocation apparatus provided by the present invention, the index determining module 24 is specifically configured to:
determining the grouping distance of every two sensors according to the initial crop bottom soil moisture index and the humidity sequence corresponding to the sensors;
and clustering the sensors by using a density clustering algorithm according to the grouping distance to obtain at least one clustering cluster as a corresponding sensor group.
Further, according to the water resource optimization distribution device provided by the invention, the index determination module 24,
if the number of the sensors in the sensor group is one, the sensors are abnormal sensors;
obtaining an index difference average value of the initial crop bottom soil moisture indexes of the abnormal sensor and other sensors in a preset neighborhood range;
comparing the standard deviation average value with a preset difference threshold value, if the index difference average value is larger than the preset difference threshold value, replacing the initial crop bottom soil moisture index of the abnormal sensor with the initial crop bottom soil moisture index average value in the preset neighborhood range, and clustering and grouping the abnormal sensors again based on the initial crop bottom soil moisture index average value;
and if the index difference average value is not greater than the preset difference threshold value, taking the abnormal sensor as an independent sensor group without clustering again.
Further, according to the present invention, the apparatus for optimizing and allocating water resources is provided, wherein the index determining module 24 is configured to determine a grouping distance between two sensors according to a first model, wherein the first model includes:
wherein D is the grouping distance, WAIs the initial crop bottom soil moisture indicator of sensor A, WBIs the initial crop bottom soil moisture indicator of sensor B, SASaid humidity sequence, S, for sensor ABFor the humidity sequence of sensor B, PPMCC () is a pearson coefficient calculation function.
Fig. 3 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 3: a processor (processor)310, a communication Interface (communication Interface)320, a memory (memory)330 and a communication bus 340, wherein the processor 310, the communication Interface 320 and the memory 330 communicate with each other via the communication bus 340. The processor 310 may call logic instructions in the memory 330 to perform a method for optimized allocation of water resources, the method comprising: acquiring humidity information near crop soil in a farmland, which is acquired by a plurality of sensors at preset sampling time; acquiring temperature information of the farmland at the preset sampling time; determining the growth period of the crops according to the types and seasonal time of the crops, and determining the crop transpiration amount according to the growth period of the crops; grouping the sensors according to the humidity information, the temperature information and the crop transpiration amount to obtain at least one sensor group and an average crop bottom soil moisture index corresponding to each sensor group; inputting the average crop bottom soil moisture index and the crop growth period into an irrigation quantity determination neural network model to obtain the irrigation quantity of a farmland area corresponding to the sensor group; the irrigation quantity determination neural network model is obtained by training based on an average crop bottom soil moisture index sample, a crop growth period sample and a predetermined irrigation quantity.
In addition, the logic instructions in the memory 330 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product includes a computer program, the computer program can be stored on a non-transitory computer readable storage medium, when the computer program is executed by a processor, a computer can execute a water resource optimization allocation method provided by the above methods, the method includes: acquiring humidity information near crop soil in a farmland, which is acquired by a plurality of sensors at preset sampling time; acquiring temperature information of the farmland at the preset sampling time; determining the growth period of the crops according to the types and seasonal time of the crops, and determining the crop transpiration amount according to the growth period of the crops; grouping the sensors according to the humidity information, the temperature information and the crop transpiration amount to obtain at least one sensor group and an average crop bottom soil moisture index corresponding to each sensor group; inputting the average crop bottom soil moisture index and the crop growth period into an irrigation quantity determination neural network model to obtain the irrigation quantity of a farmland area corresponding to the sensor group; the irrigation quantity determination neural network model is obtained by training based on an average crop bottom soil moisture index sample, a crop growth period sample and a predetermined irrigation quantity.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to execute a water resource optimization allocation method provided by the above methods, the method including: acquiring humidity information near crop soil in a farmland, which is acquired by a plurality of sensors at preset sampling time; acquiring temperature information of the farmland at the preset sampling time; determining the growth period of the crops according to the types and seasonal time of the crops, and determining the crop transpiration amount according to the growth period of the crops; grouping the sensors according to the humidity information, the temperature information and the crop transpiration amount to obtain at least one sensor group and an average crop bottom soil moisture index corresponding to each sensor group; inputting the average crop bottom soil moisture index and the crop growth period into an irrigation quantity determination neural network model to obtain the irrigation quantity of a farmland area corresponding to the sensor group; the irrigation quantity determination neural network model is obtained by training based on an average crop bottom soil moisture index sample, a crop growth period sample and a predetermined irrigation quantity.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A water resource optimal allocation method is characterized by comprising the following steps:
acquiring humidity information near crop soil in a farmland, which is acquired by a plurality of sensors at preset sampling time;
acquiring temperature information of the farmland at the preset sampling time;
determining the growth period of the crops according to the types and seasonal time of the crops, and determining the crop transpiration amount according to the growth period of the crops;
grouping the sensors according to the humidity information, the temperature information and the crop transpiration amount to obtain at least one sensor group and an average crop bottom soil moisture index corresponding to each sensor group;
inputting the average crop bottom soil moisture index and the crop growth period into an irrigation quantity determination neural network model to obtain the irrigation quantity of a farmland area corresponding to the sensor group;
the irrigation quantity determination neural network model is obtained by training based on an average crop bottom soil moisture index sample, a crop growth period sample and a predetermined irrigation quantity.
2. The method for optimized distribution of water resources according to claim 1, wherein the grouping of the plurality of sensors according to the humidity information, the temperature information and the crop evapotranspiration amount to obtain at least one sensor group comprises:
determining an initial crop bottom soil moisture index corresponding to each sensor according to the humidity information, the temperature information and the crop evapotranspiration amount;
and grouping the sensors according to the initial crop bottom soil moisture index and the humidity information to obtain at least one sensor group.
3. The method for optimized distribution of water resources according to claim 2, wherein the obtaining of the average crop bottom soil moisture indicator corresponding to each sensor group according to the humidity information, the temperature information and the crop transpiration amount of each sensor in each sensor group specifically comprises:
determining a humidity sequence corresponding to each sensor in each sensor group according to the humidity information;
determining the difference distance between any two sensors in the sensor group according to the humidity sequence;
determining a data unification coefficient of the sensor group according to the difference distance;
adjusting the initial crop bottom soil moisture index corresponding to each sensor according to the data unification coefficient to obtain an adjusted crop bottom soil moisture index;
and adding according to the adjusted crop bottom soil moisture indexes to obtain the average crop bottom soil moisture index corresponding to each sensor group.
4. The method for optimized distribution of water resources as claimed in claim 3, wherein said grouping of said plurality of sensors according to said initial crop bottom soil moisture indicator and said humidity information to obtain at least one sensor group comprises:
determining the grouping distance of every two sensors according to the initial crop bottom soil moisture index and the humidity sequence corresponding to the sensors;
and clustering the sensors by using a density clustering algorithm according to the grouping distance to obtain at least one clustering cluster as a corresponding sensor group.
5. The method for optimized allocation of water resources according to claim 4, wherein said clustering said sensors according to said grouping distance by using a density clustering algorithm to obtain at least one cluster as a corresponding sensor group comprises:
if the number of the sensors in the sensor group is one, the sensors are abnormal sensors;
obtaining an index difference average value of the initial crop bottom soil moisture indexes of the abnormal sensor and other sensors in a preset neighborhood range;
comparing the index difference average value with a preset difference threshold value, replacing the initial crop bottom soil moisture index of the abnormal sensor with the initial crop bottom soil moisture index average value in the preset neighborhood range if the index difference average value is larger than the preset difference threshold value, and clustering and grouping the abnormal sensors again based on the initial crop bottom soil moisture index average value;
and if the index difference average value is not greater than the preset difference threshold value, taking the abnormal sensor as an independent sensor group without clustering again.
6. The method for optimized distribution of water resources according to claim 4, wherein a first model is used to determine the grouping distance between every two sensors according to the initial crop bottom soil moisture indicator and the humidity sequence corresponding to the sensors, wherein the first model comprises:
wherein D is the grouping distance, WAIs the initial crop bottom soil moisture indicator of sensor A, WBIs the initial crop bottom soil moisture indicator of sensor B, SASaid humidity sequence, S, for sensor ABFor the humidity sequence of sensor B, PPMCC () is a pearson coefficient calculation function.
7. An apparatus for optimizing distribution of water resources, comprising:
the humidity information acquisition module is used for acquiring humidity information, collected by the sensors at preset sampling time, of the vicinity of crop soil in the farmland;
the temperature information acquisition module is used for acquiring the temperature information of the farmland at the preset sampling time;
the transpiration amount acquisition module is used for determining the growth period of the crops according to the types and seasonal time of the crops and determining the transpiration amount of the crops according to the growth period of the crops;
the index determining module is used for grouping the sensors according to the humidity information, the temperature information and the crop transpiration amount to obtain at least one sensor group and an average crop bottom soil moisture index corresponding to each sensor group;
the irrigation quantity determining module is used for inputting the average crop bottom soil moisture index and the crop growth period into an irrigation quantity determining neural network model to obtain the irrigation quantity of a farmland area corresponding to the sensor group;
the irrigation quantity determination neural network model is obtained by training based on an average crop bottom soil moisture index sample, a crop growth period sample and a predetermined irrigation quantity.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for optimized allocation of water resources according to any one of claims 1 to 6.
9. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the steps of the method for optimized allocation of water resources according to any one of claims 1 to 6.
10. A computer program product comprising a computer program, wherein the computer program when executed by a processor implements the steps of the method for optimized allocation of water resources according to any one of claims 1 to 6.
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